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Ghent University – iMinds – Multimedia Lab

Master thesis subjects 2015 - 2016

VIDEO

Efficient transcoding from VP9 to HEVC

Promotor: Peter Lambert and Rik Van de Walle

Supervisors: Johan De Praeter, Glenn Van Wallendael

Study Programme: Master Computer Science Engineering, Master Electrical Engineering

Number of students: 1

Number of theses: 1

Contact: Johan De Praeter

Keywords: VP9, HEVC, video compression, transcoding

Location: home, Zuiderpoort

Problem definition:

The next generation of video compression standards offers better compression than its

predecessors. However, two different standards are being pushed forward: VP9 and High Efficiency

Video Coding (HEVC). These two standards will likely coexist in the future, which makes it important

to provide compatibility between devices supporting the different standards. This compatibility can

be achieved by transcoding a bitstream encoded with one standard to a bitstream compatible with

the other. The simplest method for doing this consists of decoding the bitstream and re-encoding it

with the other one standard. However, this process can be made more efficient, since there are

some similarities between both standards.

Goals:

The goal of this master’s thesis is to reduce the complexity of transcoding from VP9 to HEVC while

retaining good video quality. To achieve this, the student will have to get acquainted with the

properties of both VP9 and HEVC to exploit similarities between both compression standards. The

student gets to build on an existing transcoding framework written in Python or C++, but can also

directly make changes to the reference software of both video standards (VP9:

http://www.webmproject.org/code/; HEVC: http://hevc.kw.bbc.co.uk/).

http://www.webmproject.org/code/

http://hevc.kw.bbc.co.uk/

Classification and detection of screen content for visually lossless transcoding in video

compositions for HEVC


Supervisors: Johan De Praeter, Thijs Vermeir



Number of theses: 1


Keywords: HEVC, lossless compression, transcoding, compositions, screen content


Problem definition:

Video sequences can either contain natural content, which is content captured by cameras, or

screen content such as a screen capture of a desktop. In many cases, such as screen sharing

during a video call, it is important to preserve the detail of this screen content to assure the

readability of data. To do this, more bits can be allocated to the entire video sequence. However, in

a composition of both natural and screen content, this would result in bits being wasted on the

natural content. Moreover, depending on the type of screen content, the amount of bitrate increase

to prevent annoying artifacts might differ. Therefore, it should be possible to detect several types of

screen content in a composition.

Goals:

In this master’s thesis, the goal is to detect and classify different types of screen content, and to

identify the minimal bitrate that should be allocated to prevent annoying artifacts to appear. In a

second phase, the student will implement this bitrate allocation by building on an existing

transcoding framework for video compositions in Python or C++ and by making changes to the

reference encoder of the HEVC video compression standard (http://hevc.kw.bbc.co.uk/).

This thesis is in cooperation with Barco (www.barco.be).


http://www.barco.be/

Content-adaptive bandwidth sharing for video transport over the network





Number of theses: 1


Keywords: video compression, quality of service, network policy, adaptive streaming


Problem definition:

In a family of five people, one child is browsing YouTube on his tablet, while another one is having a

Skype call with a friend. Meanwhile, the third child is streaming a film in full HD on his laptop. In the

living room, the parents are watching Netflix on their Ultra HD television. However, each family

member experiences an unstable video quality, since all of the aforementioned devices are

competing for their share of bandwidth. This same problem occurs whenever several streaming

clients share the same bottleneck link.

Goals:

The goal of this thesis is to investigate different policies for sharing bandwidth between different

devices on the same network during video streaming. This policy should take into account the

capabilities of the devices and the type of content and should distribute bit rates appropriately to

provide end users with an optimal quality of service. More specifically, the student will have to

analyze the relation between bit rate, quality, and the type of video content.



Consistent quality video streaming over the network





Number of theses: 1


Keywords: video compression, video quality, adaptive streaming


Problem definition:

When streaming video over the internet, the bit rate of video stream segments is usually kept

constant. However, if the video content switches from a static scene to a fast motion scene, the

quality of the latter will suffer since complex motions require more bits to encode than static content.

Depending on the content, this sudden drop of quality will be noticeable or distracting to the viewer.

To prevent such a scenario, a more intelligent distribution of the bit rate is necessary.

Goals:

The goal of this thesis is to investigate strategies to provide consistent quality during video

streaming. As one such strategy, the student can ‘steal’ bits from low-motion scenes to use in high-

motion scenes. The bit allocation algorithms can be implemented by making changes to the





Tracking down the pirates: watermarking as countermeasure against illegal video

distribution


Supervisors: Johan De Praeter, Luong Pham Van



Number of theses: 1


Keywords: HEVC, video watermarking, copyright protection, piracy


Problem definition:

Copying an unprotected digital video file is an easy thing to do. Copyrights are violated and piracy

runs rampant. In an effort to protect their properties, copyright holders encrypt their video in such a

way that only authorized decoders can play their files, limiting the playback options of their honest

users. The malicious users on the other hand focus their efforts to decrypt the secured videos and

distribute them anyway.

A much kinder approach to protect the copyright of video files is to attach a unique watermark to

each digitally distributed video file. This watermark is hidden in the bit stream while keeping the file

standard complaint. As such, honest users can use their favorite media player to watch the film,

while malicious users won’t notice the watermark that will identify them as the uploader if they dare

to illegally distribute the file.

Goals:

In a first phase, the student will identify elements in HEVC bit streams that can be modified to

contain watermarks without introducing a visible loss of quality in the video. In a second phase, the

robustness of the watermark will be tested when subjected to several attacks such as re-encoding

of the video. The watermarking algorithms can be implemented by making changes to the reference

encoder and decoder of the HEVC video compression standard (http://hevc.kw.bbc.co.uk/).


Ultra-fast HEVC encoding


Supervisors: Johan De Praeter, Glenn Van Wallendael



Number of theses: 1


Keywords: HEVC, video compression, low complexity encoding, machine learning


Problem definition:

HEVC is the successor of the H.264/AVC video compression standard. It doubles the compression

ratio of video for the same quality as its predecessor. However, this increased compression

efficiency comes with a price: the computational complexity has seen a huge increase as well. Since

HEVC will be used to encode ultra HD resolution content which can contain up to 16 times the

amount of pixels of current full HD content, it is a necessity to reduce the complexity as much as

possible. Fortunately, some decisions in the encoder can be accelerated by using data from

previously made decisions.

Goals:

The goal of this thesis is to reduce the encoding time of HEVC while keeping the compression

efficiency as high as possible. In the first phase, the student is expected to make an analysis of

techniques for fast HEVC encoding to become familiar with the encoding decisions that are viable

options for acceleration. In a second phase, the student can try different strategies such as machine

learning to skip these encoding decisions.

For the machine learning aspects, the student gets to build on an existing transcoding framework

either in Python or C++. Additionally, algorithms can also be implemented by making changes to the



Robust low-latency video encoding for distribution over error-prone networks





Number of theses: 1


Keywords: HEVC, video compression, low-latency, error-prone network


Problem definition:

In current meeting rooms, a wireless connection is used between the presenter’s laptop and the

decoder attached to the display. For a high quality of experience it is important that the stream is

displayed in the meeting room with low-latency and high quality. The wireless connection is the

bottleneck to achieve this requirement. This connection is error-prone (dropped packets), has lots of

jitter and has limited bandwidth. Multiple solutions could be proposed to minimize the impact of

dropped network packets (e.g. using a reliable network connection, using less bandwidth) but all

have their own disadvantages.

Goals:

The goal of this thesis is to analyze the influence of wireless networks on the HEVC video stream

while providing a high quality of experience. First, the student is expected to analyze the impact of

the wireless connection on this video stream and study the state-of-the-art for robust video encoding

over error-prone networks. Secondly, the student should try different strategies to improve the

quality of experience. These strategies could be related to the streaming protocol or the HEVC

codec configuration. The student can also investigate how some tools (FMO, ASO, RS) from a

previous codec (H.264/AVC) may improve the quality of the video stream.



Tracking of moving objects in the HEVC compressed domain for ultra-high resolution video


Supervisors: Johan De Praeter, Niels Van Kets



Number of theses: 1


Keywords: HEVC, object detection, video surveillance, video analysis, compressed domain


Problem definition:

Video object detection and tracking is a useful technique for video surveillance and other video

analysis applications. The most accurate way to perform this analysis is on the raw pixels in the

video. However, these techniques may be computationally complex, which is not desirable for real-

time analysis of ultra HD videos with resolutions of 3840x2160 and 7680x4320 pixels. Moreover, a

lot of videos only exist in compressed form and need to be decoded first. A less computationally

complex approach to track objects in these videos is to extract some information from the bit stream

and perform object detection on this information.

Goals:

The goal of this thesis is to detect and track objects by using only a subset of information from an

HEVC bit stream. The algorithms should have a low complexity, since they should be able to detect

and track many different objects in an ultra-high resolution video. In a first phase, the student should

investigate existing techniques for compressed domain object detection in previous video coding

standards. In a second phase, these techniques can be adapted and expanded for HEVC by

building on an existing information extraction framework.

Going Beyond HD: optimized encoding of personalized views from Beyond HD content


Supervisors: Johan De Praeter, Niels Van Kets



Number of theses: 1


Keywords: HEVC, video compression, Beyond HD, ultra-high resolution


Problem definition:

Advances in digital video capturing allow cameras to capture videos with increasingly high

resolutions. Using stitching technology, the output of these cameras can be stitched together to

create a video with a resolution far beyond HD. However, such an amount of data cannot easily be

transported to viewers at home. Additionally, the increased resolution would be wasted on the

limited capabilities of their displays. As a solution, the user can select a subset of the beyond HD

picture and zoom in on interesting events.

Since many users will be interacting with the video at the same time, it’s necessary to speed up the

encoding of each personalized stream. To achieve this, the personalized streams can exploit the

correlation with the encoding of the full beyond HD picture.

Goals:

The goal of this thesis is to reduce the encoding complexity of each personalized stream to make

the system viable for a large amount of users. The student should investigate the correlation

between personalized streams and the full beyond HD picture to skip encoding decisions with e.g.

machine learning. For the machine learning aspects, the student gets to build on an existing

transcoding framework either in Python or C++. Additionally, algorithms can also be implemented by

making changes to the reference encoder of the HEVC video compression standard

(http://hevc.kw.bbc.co.uk/).


Parallel encoding and decoding of DNA sequences on multicore CPUs and/or GPUs

Promotor: Wesley De Neve and Rik Van de Walle

Supervisors: Tom Paridaens and Ruben Verhack

Study Programme: Master Computer Science Engineering, Master Electrical Engineering, Master

Mathematical Informatics


Number of theses: 1

Contact: Tom Paridaens

Keywords: Bio-informatics, compression, DNA, Parallel processing


Problem definition:

The human genome consists of more than 3 billion base pairs. Assuming that each base pair is

stored in one byte, the human genome has an uncompressed size of 2.8 gigabyte. Add to this that

the cost of reading the human genome (i.e. genome sequencing) dropped significantly the past

years and it is clear that the amount of sequenced genomes is rising very fast. In fact, it rises faster

than the speed at which storage capacity (per $) rises. This limits the development of new DNA-

oriented applications, such as personal health care and biometric identification.

Goals:

Within Multimedia Lab, a framework has been built that compresses DNA sequences based on

principles used in the domain of video compression. This master’s thesis has the goal to improve

the efficiency (processing speed and memory usage) and effectiveness (compression ratio) of the

framework. The focus is on parallelizing the framework in order to be able to use multi-core

architectures for the compression of DNA sequences and (potentially) using the gained processing

time to apply additional compression tools to improve the effectiveness too.

Compression and streaming of DNA reads for faster exchange and processing of DNA

information


Supervisors: Tom Paridaens




Number of theses: 1


Keywords: Bio-informatics, compression, DNA, distributed processing, streaming

Location: home, Zuiderpoort, Applied Maths

Problem definition:

As the cost of sequencing genomes has dropped significantly the past years, sequencing

technology has been applied more and more to generate databases for researchers and public

health institutes, e.g. to investigate the evolution and spreading of organisms such as salmonella.

Unfortunately, the speed at which base pairs can be read is higher than the speed at which they can

be transmitted. Often, researchers use hard drives to transmit the sequencing data. This generates

delays for research and the treatment of epidemics.

Goals:

Within Multimedia Lab, a framework has been built that compresses DNA sequences based on

principles used in the domain of video compression. This master’s thesis focuses on adding support

for individual reads. These are short DNA sequences, often not longer than some 10s or 100s of

base pairs. The second goal is to transmit these individually compressed reads fast and efficiently

from the sequencing machine across a network to a central database or end-users, to allow for live

processing and streaming of data in a distributed environment.

This master’s thesis is in cooperation with Applied Maths (http://www.applied-maths.com). Applied

Maths is a Ghent-based company that creates software for management and analysis of biological

data.

http://www.applied-maths.com/

Encryption of DNA sequences with preservation of accessibility for genomic analysis


Supervisors: Tom Paridaens and Glenn Van Wallendael




Number of theses: 1


Keywords: Bio-informatics, compression, DNA, experimental research, encryption, DRM


Problem definition:


technology has been applied more and more to generate databases for researchers, e.g. to

investigate epidemics or diseases such as cancer. As this is sensitive data, protection of the privacy

of patients is very important. Encryption and DRM are key tools to protect this data.

Goals:

Within Multimedia Lab, a framework has been designed that compresses DNA Sequences, based

on principles from the video compression domain.

This master’s thesis has the goal to expand the current framework with encryption capabilities. In

the first phase, available encryption technologies and techniques will be studied. In the second

phase, methods will be designed and implemented in such a way that the data is (partially)

encrypted, but can still be used for medical research.

Complexity Constrained Scalable Video Coding


Supervisors: Thijs Vermeir and Johan De Praeter

Study Programme: Master in Computer Science Engineering, Master Electrical Engineering, Master

of Mathematical Informatics


Number of theses: 1

Contact: Thijs Vermeir

Keywords: Scalable Video, SHVC, Complexity Constrained Encoding

Location: home, Zuiderpoort or Technicum or Technicum

Problem definition:

In a multi-site meeting, consumer electronics are used a lot for sharing screen content

(presentations, documents...) between different participants. These participants could be connected

with a high bandwidth connection (e.g., other meeting room in the same building) or a low

bandwidth connection (e.g., home office, customer site). Typically these consumer electronics

(laptops, tablets, smartphones etc.) use hardware (HW) accelerated encoding and have (limited)

computational capabilities available. Encoding the screen content multiple times for every bandwidth

constraint is not always possible as only one HW acceleration block is available. If the HW

accelerated encoder is used as the base layer (low bandwidth) the available computational power

could be used to create an enhancement layer (high bandwidth).

Goals:

The goal of this master’s thesis is to investigate strategies for a complexity constrained scalable

HEVC encoder. As it is expected that the base layer is already generated with a HW encoder, the

student should concentrate only on the extension layer. In the first phase the student should study

the complexity distribution for the HEVC reference software and analyze the relation between

complexity, rate and distortion. In the next phase the student should explore strategies to reduce the

complexity taking rate and distortion into account.



DNA compression using machine learning techniques

Promotor: Wesley De Neve, Pieter Buteneers (Reservoir Lab)

Supervisors: Ruben Verhack, Tom Paridaens, Lionel Pigou (Reservoir Lab)



Number of theses: 2

Contact: Ruben Verhack

Keywords: DNA, compression, machine learning, neural networks


Problem definition:

The human genome consists of more than 3 billion base pairs. Assuming that each base pair is

stored in one byte, the human genome has an uncompressed size of 2.8 gigabyte. Add to this that

the cost of reading the human genome (i.e. genome sequencing) dropped significantly the past

years and it is clear that the amount of sequenced genomes is rising very fast. In fact, it rises faster

than the speed at which storage capacity (per $) rises. This limits the development of new DNA-

oriented applications, such as personal health care and biometric identification.

Goals:

Within Multimedia Lab, basic research has been performed to compress DNA sequences based on

principles used in the domain of video compression. In these master theses, the goal is to explore

new paths for compressing genomic data using machine learning. Machine learning algorithms are

known to be well suited for exploiting characteristics of the underlying model, i.e. the grammar of the

genomes.

The goal of these two theses is to explore the usage of machine learning in DNA compression, with

two different approaches: reference based and non-reference based. Reference based compresses

genomes relative to a reference genome. Non-reference based methods compress genomic data

without external references.

Non-transform based content adaptive image coding


Supervisors: Ruben Verhack



Number of theses: 1


Keywords: Image coding, lossy compression, image processing


Problem definition:

Due to the massive amount of visual materials that are captured and stored nowadays, image

compression remains an active field of research. Most standard image coding schemes follow the

transform-quantize-entropy coding scheme. JPEG 2000 relies on the wavelet transform, whereas

JPEG relies on the DCT transform. This has been the main focus for the last decades, but we claim

that spatial (non-transform) techniques are still not fully researched.

In adaptive systems the parameters of the encoder are adapted to the changing statistics of the

signal. There is a wide range of adaptive systems: they go from updating compression symbol

statistics to really understanding the semantics within the image. But they all have the mutual goal

of gaining compression efficiency through a better understanding of the content it is compressing.

Goals:

The goal in this master’s thesis is to develop a completely new strategy for exploiting image content

for coding purposes. This includes finding an underlying model of the image as a description and

coding it efficiently. This should be done while keeping the characteristics of the human visual

system (HVS) in mind. This complete new way of image coding provides many opportunities for

different approaches, e.g. adaptive sampling (why would you code pixels that you can predict

perfectly?), machine learning approaches (learning correlations), kernel regression (function

approximation), or dictionary learning.

The implementation can be done in Matlab, Python or C/C++. If necessary, acceleration on the GPU using

NVIDIA CUDA is possible.

Lossy content aware image coding


Supervisors: Ruben Verhack



Number of theses: 1


Keywords: Image coding, lossy compression, DPCM, prediction methods, machine learning


Problem definition:

Due to the massive amount of visual materials that are captured and stored nowadays, image

compression stays an active field of research. During the last 20 years, powerful non-linear

techniques evolved in the machine learning community around the non-linear support vector

machine theory for classification and regression of signals. Non-linear regression is intimately

related to the problem of non-linear prediction in signal theory, and thus highly relevant for predictive

coding in general. Surprisingly, this path has only been recently started gaining attention.

Our challenge is the exploration of non-linear statistical dependencies between amplitudes of image

samples for reducing redundancy of the source prior to coding while modeling the signal. In simpler

terms, using machine learning techniques, it is possible to extract the main structures in an image

by learning the model of the image. The more understanding there is about the content of an image,

the shorter it can be described, and thus coded.

Goals:

The goal in this master’s thesis is to develop a strategy for exploiting learned information from the

images for coding purposes. This includes finding an underlying model of the image as a description

and coding it efficiently. This should be done while keeping the characteristics of the human visual

system (HVS) in mind.

The implementation can be done in Matlab, Python or C/C++. If necessary, acceleration on the GPU using

NVIDIA CUDA is possible.

Video processing for interactive visualization of spherical or 360° video


Supervisors: Glenn Van Wallendael, Niels Van Kets




Number of theses: 1

Contact: Glenn Van Wallendael

Keywords: 360° video, video coding


Problem definition:

In the future, video will not be displayed on a flat surface like a TV. There will be 360° video in which

you can look around freely. For this purpose, TVs are getting curved, virtual reality glasses like the

Oculus Rift are being developed and videos get projected on spheres around viewers. Whatever the

practical solution for display will be, numerous challenges exist about providing this user with the

best possible visual experience. In an ideal world where there are no practical limitations, the entire

360° video can be provided to the end user device without any quality sacrifices. In the real world,

on the other hand, every resource is limited, but optimizations can be found in the characteristics of

this 360° video, the video content and his observer. For example, did you ever look at the eye

movement of a person turning his head? The eyes will stay focused on an object and while the head

turns different objects along the way will be fixed. Therefore, the importance of predicting the focus

points of where the observer is going to watch becomes more important. Additionally, when the

observer keeps his body stationary, he is only able to turn his head 180°. With a movie that triggers

his will to look around, the observer could start looking around further and be more active. But more

actively looking around blurs the observation capacity, so a trade-off can be made. Finally, there is

the additional advantage that the left side of the movie is connected to the right side because it

represents a sphere. This property is not being used in today regular video coders.

Goals:

In this work, the student tries to progress the scientific field of 360° video coding, transmission and

interactive visualization. In video coding perspective, the regular video gets extended with 360°

properties. To improve this 360° video experience, the restrictions of eye and head movement must

be taken into account.

Real time Ultra HD video compression using hardware/software codesign


Supervisors: Glenn Van Wallendael




Number of theses: 1


Keywords: SoC development, Ultra HD, video compression


Problem definition:

Because of the ever increasing resolution of video, software based solutions are not capable of

encoding video in real time anymore. One solution would be to make the compression algorithm

parallel and execute everything on GPUs or hardware. The problem with video compression and

compression in general is that a compression algorithm only works optimally in a serial way. Serial

processing enables the compressor to learn from previous decisions with a smaller size as a result.

Still, some parts of the video encoder can be handled in parallel like the motion estimation and the

intra prediction. Therefore, the ideal solution would be to have a software/hardware co-design in

which the general structure is kept serial with a lot of parallel hardware assistance. For this purpose,

specialized chips are developed which include an ARM processor combined with hardware

programmable parts (FPGA). With both parts available on a single chip, the hardware and software

processing can be combined efficiently.

Goals:

During this thesis, the state-of-the-art High Efficiency Video Coding (HEVC) standard will be

implemented in a hardware software combined design. For this purpose, the student can start from

the open-source reference software for HEVC video compression

(https://hevc.hhi.fraunhofer.de/trac/hevc/browser/trunk ). This software can then be run on the internal

processor of the specialized Xilinx Zynq-7000 System on a Chip. This SoC then gives the possibility

to execute certain parts in hardware and doing this efficiently will be the main challenge in this

thesis.

https://hevc.hhi.fraunhofer.de/trac/hevc/browser/trunk

Guaranteeing video quality


Supervisors: Glenn Van Wallendael, Niels Van Kets




Number of theses: 1


Keywords: video quality metrics, large data analysis


Problem definition:

Video distribution systems, like the ones from Telenet, Stevie or Netflix, have no idea about the

delivered Quality of Experience to the end user. They know what they serve the network, but have

no control on what comes out at the end-user. For this purpose, lightweight algorithms exist that

analyze the received video and form an opinion about the quality of that video (good, average, or

bad). In the past, these algorithms got developed using a limited set of settings in order to keep the

ground truth generation process manageable. This ground truth would need to be generated

through groups of people scoring all the distorted videos. In this master thesis, such a lightweight

video quality algorithm will be developed using an entirely new approach. A large database of

videos will be provided such that finally a general applicable video quality metric can be designed.

Goals:

In this thesis, the goal is to make a lightweight quality metric developed using a large data analysis

approach. For this purpose, we have already started the large database generation process which

can be extended during the thesis. Additionally, tools will be provided to extract a large part of the

incoming video information and convert it into a machine readable format. Apart from extending this

work, the main challenge is the analysis of this large dataset which will result in a good quality

metric. Such a large dataset analysis is a perfect asset when looking to the job market, because

such analysis skills can be applied on a lot of problems that occur in the field.

Rate-distortion-complexity modeling for the HEVC decoder under energy constraints of

receivers


Supervisors: Luong Pham Van and Glenn Van Wallendael



Number of theses: 1


Keywords: HEVC, power-aware video coding, decoding complexity


Problem definition:

In real-life applications, video streams might be decoded by portable devices such as mobile

phones or tablets, where the devices have time-varying battery life or different processing capacities.

Under such a scenario, the reduction in decoding complexity to adapt to the capacity of receivers

becomes a critical issue. One possible solution is to allow the encoder to generate a decoder-

friendly bit stream. The new High Efficiency Video Coding (HEVC) standard is expected to be widely

used in many multimedia applications thanks to its high compression efficiency compared with

H.264/AVC. To achieve this high coding performance, HEVC introduces several new coding

techniques resulting in extremely high complexity of both the encoder and decoder. To incorporate

this standard in real-life applications, power-aware hardware modules should be investigated and

included.

Goals:

The purpose of this work is to study and propose a rate-distortion-complexity model using statistical

and machine learning techniques for HEVC. The proposed model should be evaluated for its impact

on the compression ratio of the bit stream. Ideally, this model is included in the encoder to select the

optimal coding mode, such that a trade-off between rate-distortion and decoding complexity is

achieved with a minimal impact on the compression ratio.

Robust detection and reconstruction of damaged digital video files

Promotors: Peter Lambert and Rik Van de Walle

Supervisors: Patrick De Smet (NICC) and Glenn Van Wallendael

Study Programme: Master in Computer Science Engineering, Master of Mathematical Informatics


Number of theses: 1

Contact: Patrick De Smet (NICC)

Keywords: video analysis and processing, video reconstruction


Problem definition:

In professional and home environments, recording and storing of audiovisual data is nowadays

almost exclusively performed using digital techniques. As a result of this evolution, there is an

increasing demand towards new possibilities for analyzing digital information in the context of

forensic investigations.

A particular and common problem in these investigations is the analysis of damaged digital files due

to the complete or partial erasure of files, or due to malfunctioning or damaged data carriers and/or

hardware. As a result, police and forensic experts are facing the challenging task of tracking and

partially recovering the remaining damaged information, in the context of investigations of retrieved

or confiscated multimedia players and carriers, computer systems etc.

Goals:

The goal of this master’s thesis is to investigate and implement novel techniques in software that

allow to recover damaged digital video files from certain ‘data dumps’ in a reliable way, and where

possible, to reconstruct and decode the resulting files.

In this thesis, you will collaborate with the National Institute of Criminalistics and Criminology (NICC,

http://nicc.fgov.be/). The NICC is a scientific institute within the federal department of justice which

performs forensic research in collaboration with governments and police departments.

http://nicc.fgov.be/

User interaction with personalized views extracted from Beyond HD content


Supervisors: Niels Van Kets, Johan De Praeter



Number of theses: 1

Contact: Niels Van Kets

Keywords: Beyond HD, ultra-high resolution, user interaction


Problem definition:

Advances in digital video capturing allow cameras to capture videos with increasingly high

resolutions. Using stitching technology, the output of these cameras can be stitched together to

create a video with a resolution far beyond HD. However, such an amount of data cannot be easily

transported to viewers at home. Additionally, the increased resolution would be wasted on the

limited capabilities of their displays. As a solution, the user can select a subset of the beyond HD

picture and zoom in on interesting events.

But how will the user interact with this beyond HD picture? Does a user prefer a certain type of input

such as a tablet, jawbone or oculus rift? Or does a user prefer a more passive approach and simply

select the region or events (such as tracking the ball during a game of football) which he/she would

like to follow?

Goals:

Within Multimedia Lab, a framework has been built to extract personalized views from a very high

resolution video (8600x2160 pixels). This framework allows to pan, tilt and zoom within the video.

The goal of this thesis is to perform research towards existing or new ways of interacting with this

framework. This could be active (swiping on tablets, moving around with an oculus rift, making

gestures with a jawbone etc.) or passive (automatic feature tracking). The work of the thesis student

will be focused on two main areas. First of all, a general and reusable interface or API should be

created that allows input originating from different interaction devices. Second, different interaction

devices and automatic tracking algorithms must be compared both in terms of complexity and

usability. In a final stage, the student will subjectively compare the different interaction devices and

automatic tracking systems such that a conclusion can be drawn concerning the usability of each

solution.

Optimized transmission of compressed genomic data sequences for visualization in

Genomeview

Promotor: Wesley De Neve, Yao-Cheng Lin (PSB-UGent), and Rik Van de Walle

Supervisors: Tom Paridaens




Number of theses: 1


Keywords: Bio-Informatics, Compression, DNA, Experimental Research, Visualization,

Transmission

Location: home, Zuiderpoort, Technologiepark Zwijnaarde

Problem definition:


technology has been applied more and more to generate databases for researchers, e.g. to

investigate the evolution and spreading of organisms such as salmonella. Unfortunately, the speed

at which base pairs can be read is higher than the speed at which the sequences can be transmitted.

Often, researchers use hard drives to transmit the sequencing data. This generates delays for

research and the treatment of epidemics.

Goals:

Within Multimedia Lab, a framework has been designed that compresses DNA Sequences, based

on principles from the video compression domain. Within the PSB, a genome browser has been

developed (http://genomeview.org) that allows to study genomes by navigating through genomes,

without transmitting them completely. Currently, transmissions from the server to the viewer do not

use compression.

In this master’s thesis, the goal is to integrate both frameworks to an efficient server-client system

and to define different usage scenarios and to optimize the system in terms of compression, storage

cost, server load, network load and Quality-of-Experience (QoE).

http://genomeview.org/

Design and implement an automated platform to benchmark zapping times

Promotor: Sebastiaan Van Leuven ([email protected]) and Peter Lambert

Supervisors: Glenn Van Wallendael, Wim Baekelandt ([email protected])

Study Programme: Master Computer Science Engineering, Master Mathematical Informatics,

Master Industrial Engineering: Elektronica - ICT


Number of theses: 1

Contact: Sebastiaan Van Leuven ([email protected])

Keywords: Benchmarking, zapping

Location: home, Zuiderpoort, TPVision Zwijnaarde

Problem definition:

Each year, TV sets undergo an extensive number of tests to evaluate the performance

(benchmarks). One of those benchmarks is the time required for zapping between television

channels within a Digital Video Broadcast (DVB) environment. Since zapping times are highly

depending on a number of external factors such as the location of the TV channel within the DVB

transport stream, the type of video codec used, and the temporal position within the new video

stream it is hard to model and evaluate the theoretical zapping time for televisions.

Currently, there is no test environment available to evaluate the zapping time with a statistical

relevant procedure. An automated tool allows for an objective benchmark to assist in the research

and development of improvement broadcast tuners and decoders.

Goals:

The purpose of this master thesis is to design, implement and evaluate an automated platform to

benchmark zapping times in a Digital Video Broadcast environment (DVB C/T/S). The students

should evaluate existing components on the applicability of their use in an automated platform.

Based on this study, the student combines all the selected components in a working platform.

In a second phase, the student implements a platform in which the previously selected components

are combined. This platform should allow to benchmark commercially available television sets by

applying a statistically relevant number of tests. The platform should be capable of repeating the

same test with the same content such that different panels can be evaluated. Furthermore, to allow

for new test cases, the platform should be able to dynamically interpret different test cases. The

platform automatically evaluates the performance and visually report the results.

Lastly, the student investigates different DVB broadcast use cases, and identifies and designs

potentially synthetic corner cases in order to find the largest possible zapping time for a particular

TV set.

TP Vision is a dedicated TV player in the world of visual digital entertainment. TP Vision is part of

TPV Technology, the #1 monitor manufacturer in the world. At its Innovation Site Europe, Ghent we

design televisions of the future, for Philips and other brands. NetTV, Ambilight, Android TV and

Cinema 21:9 have all been developed in this innovative and driven environment. Recognition of our

activities is visible in numerous awards such as prestigious EISA-awards.

This master’s thesis is within the Advanced Software Development department which drives the

innovation chain from conceptualization to feasibility and prototyping, as well as leads technology

and standardization roadmaps.

mailto:[email protected]



Improving Chrome Cast by using Screen Content Coding tools for VP9


Supervisors: Glenn Van Wallendael, Tabish Siddique ([email protected])

Study Programme: Master Computer Science Engineering, Master Mathematical Informatics,

Master Industrial Engineering: Elektronica – ICT, Master Industrial Engineering: Informatica


Number of theses: 1


Keywords: VP 9, Chrome Cast, Miracast, Screen Content Coding


Problem definition:

Graphical content on computer displays (i.e., screen content) is often transmitted towards

televisions using Miracast, Chrome Cast or similar techniques. Nevertheless, screen content coding

often yields visible and annoying artifacts around edges of textures and text within an image. To

reduce these artifacts, screen content coding has been proposed as an extension to High Efficiency

Video Coding.

However, screen casting scenarios where mobile devices transmit the display towards an Android

TV make use of the open source VP 9 video codec. Therefore, no screen content coding

mechanisms can be exploited and the classical coding artifacts remain visible, reducing the quality

of experience.

Goals:

This master thesis aims at investigating screen content coding mechanisms to improve the

capabilities of the VP9 encoder for screen content.

In a first phase an analysis is performed to identify the most effective screen content coding tools

within HEVC. From this analysis, the most promising techniques are identified, taking into account

the target use case of a mobile phone to television casting scenario.

In a second phase, the identified techniques are adapted, taking into account specific VP9 encoding

tools. Alternatively, the student can identify new coding algorithms and use these to show potential

improvements.

Finally, the student shows the improvement of a VP9 encoder using screen content coding tools for

relevant content. Ultimately, the evaluated techniques can be committed towards the VP9 open

source project. During the course of this master thesis, the student is also encouraged to identify

conceptually ground breaking techniques which might not be implementable.












Low-complexity temporal transcoding for high frame rate HEVC


Supervisors: Glenn Van Wallendael, Bert Van Dam ([email protected])

Study Programme: Master Computer Science Engineering, Master Mathematical Informatics


Number of theses: 1


Keywords: HEVC, temporal transcoding, next generation action cameras


Problem definition:

Capabilities of television sets have always been limited by the underlying hardware. As the current

evolutions in media experience, such as 4K, high frame rate, and high dynamic range are rapidly

evolving, existing hardware is not capable of providing playback for such new features. Therefore,

older television sets are not able to play a large amount of newly generated (user) content.

Currently, Go Pro action cameras are capable of capturing Full HD content at 120fps, while

mainstream hardware decoders are only capable of decoding 60fps. Therefore, to allow a stable

and future proof product, new techniques should be designed to efficiently adapt the input bit

streams and allow for playback capabilities with a reduced experience.

Goals:

This master’s thesis aims at investigating novel techniques to increase the amount of media content

a television set can play. This thesis will primarily focus on temporal transcoding for HEVC. To allow

deployment of temporal transcoding techniques on existing television sets, the newly developed

algorithms should have a low-complexity.

In a first phase, the research can focus on evaluating algorithms with similar behavior as H.264/AVC.

This can be used as a starting point for scientific evaluation of future algorithms.

The student can define the second phase based on the outcome of the previous results. Either the

algorithms of the first phase are optimized, or new algorithms are investigated which explicitly

exploit HEVC specific features such as temporal stability of CU and PU structures.












Optimizing a simulcast encoder for Standard and High Dynamic Range content

Promotors: Sebastiaan Van Leuven ([email protected]) and Peter Lambert

Supervisors: Glenn Van Wallendael

Study Programme: Master Computer Science Engineering, Master Mathematical Informatics


Number of theses: 1


Keywords: HEVC, HDR, EOTF, Netflix


Problem definition:

Over-the-top (OTT) content providers such as Netflix create a plethora of versions of the same

content. Currently, a different version of the content is transmitted to devices based on the

capabilities in terms of resolution, frame rate, memory size, audio support, etc. As the current hype

of 4K is fading out, a new technology has arrived. High Dynamic Range (HDR) allows to improve

the perceptual quality and immersivity by introducing more luminosity in the picture. This requires

televisions which can be 40 times as bright as current 4K televisions. However, encoding such

content requires yet again processing power for another encoder. As a large amount of the visuals

data is similar between Standard Dynamic Range (SDR) and HDR, optimizations between both

encoding loops can reduce the complexity and power consumption significantly.

Goals:

In a first phase, the student investigates the benefits and properties of HDR content. The research

focuses on HDR content captured and mastered using a new Electro Optical Transfer Function

(EOTF) as defined by SMPTE ST 2084, which is basically applying a different Gamma correction to

the decoded signal. This new EOTF changes the properties of the pixel values, such that different

encoding decision are made.

In a second phase an investigation of the different encoding decision between SDR and HDR

content is evaluated and mapped, which can be done using machine learning tools. From this

analysis a fast mode decision model is created and evaluated in an encoder.

To demonstrate the scalability of the proposed model, a single loop encoder can be designed where

the SDR and HDR content are encoded simultaneously using the proposed model. This can be

implemented in a real-life HEVC encoder such as x265.






This master thesis is within the Advanced Software Development department which drives the

innovation chain from conceptualization to feasibility to prototyping, as well as leads technology and

standardization roadmaps.



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