implementation of ccsds recommended standard...

ISSN: 2278 – 7798 International Journal of Science, Engineering and Technology Research (IJSETR)

Volume 5, Issue 5, May 2016

Implementation of CCSDS Recommended Standard

for

Image DC Compression

Sonika Gupta

Post Graduate Student of Department of Embedded System Engineering

G.H. Patel College Of Engineering and Technology Gujarat Technological University

Vallabh Vidyanagar, India

Abstract - Image compression is an important requirement of

imaging payloads on Earth Observation satellites. For satellite

image compression, the Consultative Committee for Space Data

Systems (CCSDS) had proposed an image compression standard

which is most widely implemented in hardware. This

compression consists of a two dimensional discrete wavelet

transform of the image, followed by bit-plane encoding of the

transformed data. Bit Plane encoding comprises of DC coding

and AC coding. This paper presents approach towards

implementation of DC Coding. Some simulation results using

MATLAB tool, and few modules implemented on Hardware are

given in this paper.

Index Terms –CCSDS, DC coding, BPE, gaggle,

Xilinx Virtex-5, block, segment.

I. INTRODUCTION

Image compression is one of key technologies for

imaging instruments. It is an important issue in the

communication between spacecraft and ground. The main

objective of image compression is to remove most of the

redundancies in the data and to encode the information in the

least number of bits. The approach for data compression is

believed to be the solution to the “Bandwidth Versus Data

Volume” dilemma.

A. Types of Compression

1) Lossy Compression: This compression technique

reduces an image file by permanently eliminating certain

information, especially redundant information. When the file

is uncompressed, only a part of the original information is still

there (although the user may not notice it). 2) Lossless Compression: Every single bit of data that

was originally in the file remains after the file is

uncompressed. All of the information is completely restored.

The original data can be reproduced exactly.

B. Types of DC Compression

1) DCT-Based Compression: Discrete cosine

transform (DCT) is for converting a signal into elementary

frequency components. DCT-based image compression relies

Geetali Saha

Faculty

Department of Electronics and Communication G.H. Patel College Of Engineering and Technology

Gujarat Technological University Vallabh Vidyanagar, India

on two techniques to reduce the data required to represent the

image. The first is quantization of the images DCT

coefficients; the second is entropy coding of the quantized

coefficients. Quantization is the process of reducing the

number of bits needed to represent it. Entropy coding is a

technique for representing the quantized data as compactly as

possible. 2) Wavelet-Based Compression: It comprises of lossy and lossless image compression. There

are several methods of lossy compression: EZW (embedded

zero tree wavelet) algorithm, SPIHT (set partitioning in

hierarchical trees) algorithm, WDR (wavelet difference

reduction) algorithm, and ASWDR (adaptively scanned

wavelet difference reduction) algorithm[2]. Basic algorithm is

to digitize the source image into a signal s, which is a string of

numbers. Decompose the signal into a sequence of wavelet

coefficients w. Threshold to modify the wavelet coefficients

from w to another sequence w'. Quantize to convert w' to a

sequence q, then entropy code to compress q into sequence e.

II. CCSDS STANDARD A. Overview

This Recommended Standard defines a particular payload

image data compression algorithm that has wide spread

applicability to many types of instruments. This

Recommended Standard does not attempt to explain the theory

underlying the operation of the algorithm. The algorithm consists of two functional modules as

depicted in Figure 1. Discrete Wavelet Transform (DWT) is

for de-correlation, and a Bit-Plane-Encoder (BPE) used to

encode this de-correlated data[1]. B. Discrete Wavelet Transform

Wavelet transforms continuous or discrete time

domain signals into a time-frequency domain signal. A DWT

convolves a signal against certain specific wavelet filter

coefficients, resulting in compression of original signal.

Decompression can be done via IDWT by convolving the com

pressed signal against an inverted order of original wavelet

filter coefficients[1].Discrete Wavelet Transform(DWT) is

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performed to de correlate the original data.

Fig .1-The two functional modules of the algorithm

LL3 HL 3 HL2

LL1 HL1 LL2 HL2 HH

HL1 LH3 3 HL1

LH2 HH2

LH2 HH2

LH1 HH1 LH1 HH1 LH1 HH1

Level 1 DWT Level 2 DWT Level 3 DWT

Fig. 2-Three Level 2-D DWT decomposition of an image C. Bit Plane Encoder

The wavelet transformed coefficients of an original image is reorganized into blocks where each block comprises

of 64 coefficients. A block comprises of coefficients from all

the sub bands generated by DWT. The top most left

coefficient is DC coefficient which is single in each block and

it is in LL3 sub band. The rest are called AC coefficients,

which are taken from other sub bands as hierarchical structure

given by DWT. Bit Plane encoding is categorized into DC

coding and AC coding. Due to different nature of DC and AC

coefficients, they are encoded with different techniques [3].

BPE gives more importance to the DC coefficients since most

of information is contained in these coefficients. Resulting sub

bands after DWT are stored in DWT memory. Once all ten

sub bands are available, the DC coefficients are read by DC

coder and then rice coding is carried out on quantized samples

of DC coefficient. This is followed by AC coding. AC coding

is done in four stages (1 to 4) as per standard given[1].

III. DC CODING A. Overview

The initial coding of DC coefficients in a segment is performed in two steps. First step is encoding of quantized DC

coefficient using Rice Coding described in next section.

Second step is to encode additional bits of DC coefficient, when . The remaining

bits of DC coefficient of a segment are encoded

in stage0 of the Bit Plane Encoding. The amount of quantization of DC coefficients performed in

coding step shall be determined by the dynamic range of the

AC and DC coefficients in a segment via the integer parameter

as in Table 1.

Rice Coder is a variable length coder and output bit

stream is packed into 8 bits before writing it into memory.

Refinement bits are also concatenated when writing into

memory. Serial approach for coding and packing can be

followed because there is only one DC coefficient in a block of

64 coefficients. The first quantized DC coefficient for every

sequence of S consecutive coefficients, referred to as a

reference sample, shall be written to the encoded bit stream

directly (i.e., without any further processing or encoding).

Table.1: DC Coefficient Quantization

DC and AC q' value Remark

Dynamic Range

DC dynamic range is

very small; no

quantization is

performed-all DC

coefficient information

is encoded in this step.

DC dynamic range is

close to half the AC

dynamic range: the 3

MSBs of the DC coefficients are

differentially coded.

DC dynamic range is

much higher than half

the AC dynamic

range: the 10 MSBs of

the DC coefficients are


otherwise DC dynamic range is

moderately higher

than half the AC

dynamic range: the

DC coefficient bits

exceeding half the AC

dynamic range are


B. Algorithm DC coefficients are represented using two‟s-

complement representations. Let denote the DC

coefficient in a segment. Then the number of bits required to

represent in two‟s-complement is given in equation.

, if , if




Where is the maximum of this value over all

DC coefficients in the segment. Similarly is

calculated for AC coefficients. The first step, quantization of

DC coefficient, is done where DC quantization factor is

defined as given by the equation.

where is used to indicate the number of LSBs

in each coefficient of the sub band LL3 that are necessarily

zero as a result of sub band scaling operation[1]. The value of indicates the number of LSBs in each DC

coefficient that need not to be encoded in the quantized DC

coefficient values. For a given sequence of DC coefficients

{ : =0,…, S -1} in a segment, the quantized coefficient

is given in equation.

Next find the number of bits (N) needed to represent each

quantized DC coefficient by given equation.

When N is 1, each quantized DC coefficient consists of a

single bit. Otherwise N>1 and the quantized DC coefficients

in a segment, encoded with procedure explained here. The

first quantized DC coefficient for every sequence of S

consecutive coefficients, referred to as a reference sample,

shall be written to the encoded bit stream directly (i.e., without

any further processing or encoding). For the remaining S–1 DC coefficients, the difference

between successive quantized coefficient values (taken in

raster scan order) shall be encoded.

if if

, otherwise

otherwise where

and ,

Each gaggle contains up to 16 mapped quantized coefficient

. (a) The first gaggle contains 15 values of (the first

quantized DC coefficient is coded directly as a reference

sample); (b) The remaining gaggles each contain 16 values of

, with the possible exception of the last gaggle, which may

have fewer; (c) If S is not a multiple of 16, then the last

gaggle contains J values of , where J equals S mod 16. The value of in each gaggle is encoded using one of

several code options as in Table.2, ranging from uncoded(each

is encoded using the conventional N-bit unsigned binary

integer representation) to coded via one of the several

variable-length codes parameterized by k; k>0 & k ϵ Z.

The code option selected for the gaggle shall be indicated at

the start of the coded quantized DC coefficients in the gaggle

using the appropriate code option identifier selected[5] as

shown in Fig. 3 and Fig. 4.

When the uncoded option is selected, the coded gaggle is fixed

in length, consisting of the option ID field, optionally followed

by an N-bit reference sample.

Otherwise, given code parameter k, the variable-length

codeword for has two parts. The first part shall consist of zeros followed by a 1, where . The second part

shall consist of the k LSBs of the binary representation of .

Select the optimum value of k for each gaggle (i.e., select the

value of „k‟ that minimizes the number of encoded bits)

Table. 2: Code Option Identifier Coded Data Format for a Gaggle when uncoded Option is

selected

Fig .3 Coded Data Format for a Gaggle

Coded Data Format for a Gaggle When a Coding Option is

selected

Fig .4 Gaggle in segment

C. VHDL Implementation

DC coding is implemented in VHDL and output is checked on computer using MATLAB. Fig. 6 to 11 shows the

sequential flow of implementation results




Fig.5 BLOCK DIAGRAM

IV. RESULTS

Fig .8 DC Compression on Logic Analyzer

Fig .6 VHDL Results of Top module

Fig .7 MATLAB result for DC Coding




V. CONCLUSION

This paper describes in brief the CCSDS Recommended Image

Data Compression Technique for space application taken under

ISRO Project. DC Compression coding is VHDL implemented

on Xilinx Virtex-5 FPGA Board[4] output is seen on Tektronix

Logic analyzer and the decompressed image is checked with

MATLAB Tool . This work of compression gives compressed

image of less quality, sharpness of image is somewhat reduced

and artifacts are introduced. In future scope of this work, quality

enhancement of image compression can be done by using AC

coding i.e.; bit plane encoding method.

REFERENCE [1] Image Data Compression, Recommendation for Space Data Systems

Standards, CCSDS 122.0-B-1, Blue Book, Issue 1. Washington DC CCSDS, November, 2005, available from www.ccsds.org.

[2] Image Data Compression, Information Report for Space Data Systems Standards, CCSDS 120.1-G-1, Green Book, Issue I. Washington DC CCSDS, June 2007, available from www.ccsds.org.

[3] Li Li, Gang Zhou, Bjorn Fiethe, Harald Michalik, and Bjorn Osterloh. ”Efficient implementation of the CCSDS 122.0-B-1 compression standard o a space-qualified field programmable gate array”. In: Journal of Applied Remote Sensing 7.1 (2013), pp. 074595-074595.

[4] Xilinx, Inc. Radiation-Hardened, Space- Grade Virtex-5QV Device Overview.2011.

[5] Pen-Shu Yeh, Philippe Armbruster, Aaron Kiely, Bart Masschelein, Gilles Moury, Christoph Schaefer. Carole Thiebaut. “ The New CCSDS Image Data Compression Recommendation” Proc. of the IEEE 2005 Aerospace Conference. Big Sky. Montana, March 2005.

[6] Image Data Compression, Recommendation for Space Data Systems Standards, CCSDS 122.0-B-1, Blue Book, Issue 1. Washington DC CCSDS, November, 2005, available from www.ccsds.org.





[11] Image Data Compression, Recommendation for Space Data Systems Standards, CCSDS 122.0-B-1, Blue Book, Issue 1. Washington DC CCSDS, November, 2005, available from www.ccsds.org.





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