Distributed Arithmetic Discrete Cosine Transform

BYB.Satish Kumar (10N71A0405)

Introduction To VLSI

Acronym of VLSI Very-Large-Scale Integration

A VLSI contains more than a million or so switching devices or logic gates

Early in the first decade of the 21st century, the actual number of transistors has exceeded 100 million

A piece of silicon (a chip) is typically about 1 centimeter on a side

HDL (Hardware Descriptive Language)

HDL is a specialized computer language used to describe the structure, design and operation of electronic circuits, and most commonly, digital logic circuits.

The two most widely-used and well-supported HDL varieties used in industry are ”Verilog(verify logic)” and ”VHDL( (Very High Speed Integrated Circuit) Hardware Description Language)”.

VLSI APPLICATIONS: VLSI is an implementation technology for electronic circuitry -

analogue or digital. It is concerned with forming a pattern of interconnected

switches and gates on the surface of a crystal of semiconductor.

Microprocessorspersonal computersmicrocontrollers

Memory - DRAM / SRAM Special Purpose Processors - ASICS (CD players, DSP

applications) Optical Switches Has made highly sophisticated control systems mass-

producable and therefore cheap.

INTRODUCTION TO XILINX ISE

The ISE® Design Suite is the Xilinx® design environment, which allows you to take your design from design entry to Xilinx device programming.

WHY XILINX ISE?

The ISE Design Suite: Logic Edition allows you to go from design entry, through implementation and verification, to device programming from within the unified environment of the ISE Project Navigator or from the command line.

DESIGN MODULES IN XILINX:

VERILOG MODULE

VHDL MODULE

VERILOG MODULE:Verilog, standardized as IEEE

1364, is a hardware description language (HDL) used to model

electronic systems.

It is most commonly used in the design and verification of digital circuits at the register-transfer

level of abstraction.

It is also used in the verification of analog

circuits and mixed-signal circuits.

OVERVIEW OF PROJECT

Data compression

Compression techniques

Introduction to DA-DCT

ECAT ARCHITECTURE

DA-Butterfly-Matrix

DATA COMPRESSION

Despite the many advantages of digital representation of signals compared to the analog counterpart, they need a very large number of bits for storage and transmission.

For Example, a high-quality audio signal requires approximately 1.5 megabits per second for digital representation and storage.

A television-quality low-resolution color video of 30 frames per second with each frame containing 640 x 480 pixels (24 bits per color pixel) needs more than 210 megabits per second of storage.

As a result, a digitized one-hour color movie would require approximately 95 gigabytes of storage. The storage requirement for upcoming high-definition Television (hdtv) of resolution 1280 x 720 at 60 frames per second is far greater.

CODEC

The compression and decompression systems together called a CODEC.

TYPES OF COMPRESSION:

LOSSLESS COMPRESSION

Depending on the output requirements either of the compression techniques are used.

COMPRESSION TECHNIQUES

INTRODUCTION TO DA-DCT

DISTRIBUTED ARITHMATIC-

DISCRETE COSINE TRANSFORM

DCT(DISCRETE COSINE TRANSFORM):

Discrete cosine transform (DCT) is a widely used tool in image and video compression applications.

Recently, the high-throughput DCT designs have been adopted to fit the requirements of real-time application.

STEPS INVOLVED IN COMPRESSION OF THE IMAGE

Dividing the image into pixels and level shifting them.

DCT is performed

on the pixels

resulting in DCT

blocks.

Quantization is

done on these

blocks.

Quantized matrices are arranged in

zigzag order to differentiate

low frequency components

and high frequency

components.

The encoder

block encodes

the quantized

data

Zero run and huffman coding

techniques are used to encode the

data in encoder

block.

REDUNDANCY CODINGTo remove the unnecessary data while coding is called as Redundancy Coding.

Types of Redundancies

Coding redundancy

DISCRETE COSINE TRANSFORMATION

The forward and inverse 2-D DCT can be written as:

where x(ij) is the image pixel data, and Z(u,v) is the transport data

COMPUTATION OF DCT

The 8 x 8 DCT coefficient matrix can be written as:

Even rows of C are even-symmetric and odd rows are odd-symmetric.

By exploiting this symmetry in the rows of C and separating even and odd rows we can get 1D-DCT as follows:

For 2-D DCT computation of a 8x8 2-D data, first row-wise 8x1 1-D DCT is taken for all rows followed by column-wise 8x1 1-D DCT to all columns. Intermediate results of 1-D DCT are stored in transposition memory.

DCT IMPLEMENTATION

16 POINT DCT:

ECAT ARCHITECTURE

Error-Compensated Adder Tree

ECAT Architecture For Distributed Arithmetic Based DCT

• DA-based architecture and the proposed ECAT to achieve a high-speed, small area and low-error design.

• It is proposed to compensate for the truncation error in high-speed applications.

• Reduces the shifting and addition computation time .

Two TypesMP

P (MSBs)TP

T (LSBs)

• P-bit words operate the shifting and addition in parallel

• The output Y will obtain the P-bit MSBs using a rounding operation called Post Truncation (Post-T).

• TP is usually truncated to reduce in parallel shifting and addition operations, known as the Direct truncation (Direct-T).

Proposed Error-Compensated Scheme

Where is the compensated bias from the TP to the MP

Proposed ECAT Architecture

• Full - Adder • Cell with three inputs (a, b, c)• Two outputs, a sum (s) and a carry-out (co)

• Half - Adder• Two inputs (a &b) • Two outputs, a sum (s) and a carry-out (co)

Proposed ECAT architecture of shifting and addition operators

PROPOSED 8*8 1-D DCT CORE DESIGN

xm Denotes the input data.Zn Denotes the transform output.

Above equation can be divided into even and odd parts: Ze and Zo

DA-based computation, the coefficient matrix Cee and Ceo

DA-Butterfly-Matrix

The proposed 2-D DCT is designed using two 1-D DCT cores and one transpose buffer

GENERAL IMPLEMENTATION FLOW

RTL Schematic View

Register Transfer Logic of DA-DCT

Simulation results of Distributed Arithmetic DCT

Device Utilization Summary:

Timing Summary: Speed Grade: -4• Minimum period: No path found• Minimum input arrival time before clock: 23.566ns• Maximum output required time after clock: 4.283ns• Maximum combinational path delay: No path found

ADVANTAGES

The main advantage of compression is that it reduces the data storage requirements. It also offers an attractive approach to reduce the communication cost in transmitting high volumes of data This significantly aids in reducing the cost of communication due to the data rate reduction.Hence the audience can experience rich-quality signals for audio-visual data representation.

we can receive toll-quality audio at the other side of the globe at a much better price compared to a decade ago.A single 6 MHz broadcast television channel can carry HDTV signals.Because of the reduced data rate offered by the compression techniques, computer network and Internet usage is becoming more and more image and graphic friendly, rather than being just data- and text-centric phenomena.

SECONDARY ADVANTAGES

Data compression has great implications in database access.Data security can also be greatly enhanced by encrypting the decoding parameters and transmitting them separately from the compressed database files.An extra level of security can be achieved by making the compression and decompression processes totally transparent to unauthorized users.

The rate of input-output operations in a computing device can be greatly increased due to shorter representation of data.Data compression obviously reduces the cost of backup and recovery of data in computer systems by storing the backup of large database files in compressed form.The advantages of data compression will enable more multimedia applications with reduced cost.

DISADVANTAGES

Data compression depending on the application area and sensitivity of the data.The extra overhead incurred by encoding and decoding processes is one of the most serious drawbacks of data compression, which discourages its usage in some areas.This extra overhead is usually required in order to uniquely identify or interpret the compressed data. Data compression generally reduces the reliability of the data records.

Transmission of very sensitive compressed data (e.g., medical information) through a noisy communication channel (such as wireless media) is risky because the burst errors introduced by the noisy channel can destroy the transmitted data.Disruption of data properties, since the compressed data is different from the original data.In many hardware and systems implementations, the extra complexity added by data compression can increase the system’s cost and reduce the system’s efficiency.

FUTURE SCOPE

This work can be extended in order

to increase the accuracy by

increasing the level of

transformations.

This work can improve by implementing JPEG 2000

Image compression standard in which ordinary

DCT transformation part can be replaced by our Distributed Arithmetic

Discrete Cosine Transform (DA DCT) Design.

This can be used as a part of the block in the full fledged application, i.e., by using these DA DCT, the applications

can be developed such as compression,

watermarking, etc.

The 8-Point Distributed Arithmetic Discrete Cosine

Transform (DA DCT) Design can be made to 16,

32-point Distributed Arithmetic Discrete Cosine

Transform (DA DCT) by making minor

modifications to the code.

CONCLUSION

This paper presents an efficient architecture for computing the 2-D DCT with distributed arithmetic. The proposed architecture requires less hardware than conventional architectures which use the original DCT algorithm or the even-odd frequency decomposition method.

The modules of the transpose memory and parallel Distributed Arithmetic 2-D DCT architecture were designed and synthesized. The paper contributed with specific simplifications in the multiplier stage, by using shift and add method, which lead to hardware simplification and speed up over architecture.

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