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ECE 5578 Multimedia Communication Lec 01 - Introduction to Image and Video Coding
Zhu LiDept of CSEE, UMKC
Office: FH560E, Email: [email protected], Ph: x 2346.http://l.web.umkc.edu/lizhu
ECE 5578 Multimedia Communication, 2020. p.1
slides created with WPS Office Linux and EqualX equation editor
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Outline
Background Objective of the class Prerequisite Lecture Plan Course Project Q&A
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Video Growth
Better and Bigger Video: Moving to UHD 4K resolution HDR – High Dynamic Range Much higher data rate
Mobile Video: 25X growth predicted over the next 5 years
(source Cisco Visual Networking Index) Low power encoding/decoding on devices Mobile network capacity gap
Internet Video: Already accounts for more than half the
internet traffic Netflix alone 70% of ISP traffic at peak
time. Need better coding efficiency
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Video Signal
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Time
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Video Compression
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[Source: ISCAS 2014 tutorial, V.Sze and M. Budagavi]
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Compression Approaches Basics of Video Compression Remove redundancy from the signal – brain to eye connection is
estimated to have only several hundred bits/sec capacity.
Video Signal Redundancy: Spatial Redundancy Perceptual Redundancy Temporal Redundancy Symbol Statistical Redundancy
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Spatial Redundancy Removal
Intra Prediction – predict within the frame
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Spatial Redundancy Removal
Block Transform:
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Perceptual Redundancy
Human Visual System (HSV) is more sensitive to changes in low freq. areas than high freq. Capitalize on this by allow for coarser quantization at different
texture areas.
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Perceptual Redundancy Removal
Quantization
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Quant table
Quantized DCT coeff
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Temporal Redundancy
Exploit redundancy among successive frames
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Naïve !
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Motion Compensation
Block based motion compensation for Temporal Redundancy removal
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Video Motion Prediction Structure
I, P, B, b Frames…
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Symbol Stats Redundancy
Not all pixel values have the same frequency Say if 128 appears 90% of time, we can use short bits to represent, and
longer bits to represent the rest of symbols Entropy coding !
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Video Compression in Summary
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Video Coding Standard
Why Standard ?
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Video Coding Standard History
Pre-HEVC
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Rate-Distortion Performance
Pre-HEVC
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HEVC – High Efficiency Video Coding
Objective of HEVC
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HEVC History
Very brutal…
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HEVC Performance
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HEVC Resources
Overview Paper: G. J. Sullivan, et al. "Overview of the High Efficiency
Video Coding (HEVC) standard,” IEEE Transactions on Circuits and Systems for Video Technology, 2012
Spec: H265 High Efficiency Video Coding Specification http://sist.sysu.edu.cn/~isscwli/ref/h265.pdf
Reference Software: – https://hevc.hhi.fraunhofer.de/svn/svn_HEVCSovware/
Python Tool: HARP http://lms.lnt.de/HARP/
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New Media – 3D Image/Video
Point Cloud Capture & Compression
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Passive Camera Array Active Lidar/Radar
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Point Cloud Capture and Compression
Use Cases Content Capture for VR/AR
o Replay Technology (Acquired by Intel)o 8i.com
Navigationo Point cloud map for navigation
Challenges Point Cloud Static and Dynamic Geometry
Compression Point Cloud Attributes Compression
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Video-based point cloud compression
Basic steps Normal-based projection, frame packing, and frame padding
Normal-based projection Organize the points with similar normal into a patch Project each patch to the 3D point cloud bounding box
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Video-based point cloud compressionFrame packing: pack the patches into frames Exhaustive search empty space for the current patch Patch rotation is supported Introduced a lot of sharp edges
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VPCC - Texture PaddingTexture padding: a number of methods are proposed to minimize
the bitrate of the unoccupied pixelsUsing push-pull algorithm as an example, like dilation
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Video-based point cloud compressionBasic idea: project a point cloud to a 2-D video for an efficient
compression
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Geometry
Attribute
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Video Communication
The Dimension of the Video Communication Problem: Content: managed vs UGC Transport: managed IP networks vs Over The Top (OTT) networks Devices: managed settop boxes, vs unmanaged devices
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[Christian Timmerer & Ali Begen, ICME 2015 Tutorial]
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Current Landscape
Managed vs Unmanaged:
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[Christian Timmerer & Ali Begen, ICME 2015 Tutorial]
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Traditional TV vs OTT Video
Services are converging…
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Netflix Phenomena
Purely OTT Solution, very successful in user penetration Accounted for a large portion of ISP traffic between 8-10pm.
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HBO
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Internet is dominated by video traffic
Top Down/ Bandaid approach: CDN (Content Delivery Network) Solutions, mostly based on HTTP
transport
New Trend/Clean Slate Approach: ICN (Info Centric Networking), design from layer 3 up to support
video
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PSS over managed IP networks
Managed mobile core IP networks
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MPEG DASH – OTT
HTTP Adaptive Streaming of Video
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Quality of Experience (QoE)
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QoE Spatial quality Temporal quality Viewing condition Packets received
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Prerequisite & Text book
Prerequisite Good C and Python programming skills Taken Digital Signal Processing and/or Digital
Image Processing, or consent of the instructor Will have different expectation for MS and
undergrad students
Textbook: None required (saving $$) , will distribute relevant
chapters, papers, and notes.
Key References: Y. Wang, J. Ostermann, and Y.Q.Zhang, "Video Processing and
Communications," 1st ed., Prentice Hall, 2002. ISBN: 0130175471. K. Sayood, Introduction to Data Compression, 3rd Edition
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Tentative Plan
Video Coding1) Entropy and Info Theory Background2) Lossless Coding/Entropy Coding: Hoffman, Arithmetic3) Lossy Image Coding: Transforms, Quantization, Wavelet/JPEG20004) Video Signal Processing: Sampling, Motion Compensation5) Scalability and Super Resolution6) Video Coding Standards: HEVC
Video Systems/Networking1) QoE metrics,Source-Channel Coding and Error Resilience2) Media Transport: RTP/RTSP/RVSP/RTCP, HTTP/Websocket, WebRTC3) Congestion Measurement and Control: TCP, SCTP, SPDY, NADA4) CDN, HTTP Cache, Cache Deduplication and delivery acceleration5) OTT Video Streaming Standards: DASH, MMT6) Overlay networks and P2P video streaming
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Course Outcome
Upon completion of the course you will be able to: Understand the basic compression theory and algorithms for media
compression. Familiar with the latest media compression and communication
solutions and have hands on experiences. Can apply the knowledge an algorithms to solve real world media
communication problems Have good job and internship prospect with both Semi Conductor
Makers like QCOM, MediaTEK, Samsung, Intel, Broadcom, and content provider and CDN/ISP operators like AKAMAI, Netflix, Youtube, COMCAST, HBO, Sprint, ATT.
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Grading
Homeworks (40%) Lossless coding of symbols Lossy image coding Motion Compensation Video codec excercise Network simulation of video streaming
Quiz (30%) : relax, quiz is actually on me, to see where you guys stand Quiz-1: On Video Coding Quiz-2: On Video Communication
Project (30%) Original work leads to publication (1.25x factor), discuss with me by
Spring break Regular project: assign papers to read, implement certain aspect, and
do a presentation
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Logistics
Office Hour: Tue: 4:00-5:30pm, 560E FH Or by appointment
TA: TBD Office Hour/Lab Sessions are planned to cover certain software tools
aspects. Office Hour: TBA
Course Resources: Will share a box.com folder with slides, references, data set, and
software
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Q&A
…..
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