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A Lossless Color Image Compression Algorithm

with Adaptive Arithmetic Coding Based on Adjacent Data Probability

Chien-Pen Chuang, Guan-Xian Chen, Yi-Tsai Liao*, Chia-Chieh Lin

Department of Applied Electronics Technology,*Department of Industrial Education,

NTNU

Taipei, Taiwan

chuangglory@gmail.com, 698750152@ntnu.edu.tw, name9061@yahoo.com.tw, 698750035@ntnu.edu.tw

AbstractIn this paper, we propose a simple lossless algorithm

which can compress all types of images with the same high

Compression Rate. The algorithm consists of two phases. First,

it removes the correlation between pixels with Snake Scan to

get residual of data. And then encode the residual of data with

an Adaptive Arithmetic Coding. This Adaptive Arithmetic

Coding only uses adjacent data to build the probability model.24 color images provided by Kodak Company were used to test

compression rate of this proposed algorithm. The results show

the efficiency of this proposed algorithm is better than original

Adaptive Arithmetic Coding method.

Keywords- lossless data compression; arithmetic coding;

adjacent data probability; adaptive arithmetic coding.

I. INTRODUCTIONWhen Lossless Data Compression is employed, owing to

every data need to be preserved, it is thereby limited to makesignificant breakthrough on the compression rate. Forreducing the size of complex and valuable images to bestored, such as medical images or precious drawings, it is

essential to improve Lossless Data Compression technique.The prior art of coding methods including Run LengthCoding, Huffman Coding, Lempel-Ziv-Welch Coding (LZW)and Arithmetic Coding (AC) etc [6] all have contribution tothat. Wherein, AC, so to speak, is an effective coding methodfor Lossless Data Compression, owing to its prefix-free andwithout look-up table in decoding. Its advanced versionssuch as Adaptive Arithmetic Coding (AAC), BinaryArithmetic Coding [2], Integer Arithmetic Coding [3] etchave been frequently employed.

Wherein Context Adaptive Binary Arithmetic Coding isa part of H.264 Advanced Video Compression (H.264/AVC).It is characterized primarily in the concept of addingexpected value, so as to use precise probability estimation

under status of having sufficient coding data, for raisingcompression effect of AC, such as Distributed ArithmeticCoding extended from Distributed Source Coding ofSlepian-Wolf and Adaptive Distributed Arithmetic Coding[5]. The MQ coder applied for JPEG is such coding whichuses integer arithmetic coding or binary arithmetic coding.

In the process of image compression, if the relativityamong adjacent pixels were utilized, Repeat Reductionstorage can be reached, so as to conduct compression withRLC and AC [8]. Or conduct coding for image data, firstlythrough DCT conversion and then block process.

II. ADJACENT DATA PROBABILITYON ADAPTIVE ARITHMETIC CODING

FORLOSSLESS IMAGE COMPRESSION ALGORITHM

In the study, Adjacent Data Probability on AdaptiveArithmetic Coding for Lossless Image Compression

Algorithm (ADAACICA), also known for short as ADAAC,was put forward. The algorithm employed AdaptiveArithmetic Coding (AAC) for Lossless Data Coding as basicframework, wherein it is enhanced specific to the portion ofappearance probability of statistical symbols, to reduceaccumulation problem in coding, so that after ADAACcompression, all types of image data, in addition to the mostbasic complete reappearance, can have better compressioneffect, the process is shown as in Fig. 1.

Figure 1. ADAAC compression block diagram.

ADAAC compression algorithm is primarily dividedinto three parts, part 1, to obtain Adjacent Difference bysubtracting each other for adjacent data [7][8], with twoinformation acquired, wherein number one is Residual afteradjacent data subtracting each other, number two is MarkingMap which records plus minus signs of the values after

subtracting each other; part two, with Residual data,Building Different Bit-plane Model; part three, withMarking Map, it is directly written into compression file,while with Different Bit-plane Model (DB Model), itemploys adjacent data probability model coding, to achieveLossless Data Image Compression.

The tested compression file was a 24 bit MMP file, onthe other hand, an image compression file format wasdefined, in the File Header, it includes image format nameand image Height and Width, totally 12 Bytes.

2012 International Symposium on Computer, Consumer and Control

978-0-7695-4655-1/12 $26.00 2012 IEEE

DOI 10.1109/IS3C.2012.44

141

2012 International Symposium on Computer, Consumer and Control

978-0-7695-4655-1/12 $26.00 2012 IEEE

DOI 10.1109/IS3C.2012.44

141

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Figure 2. ADAAC file format.

In the study, AAC symbol probability statistical methodwas changed to Adjacent Data Count method, referred to asAdjacent Data Probability Adaptive Arithmetic Coding(ADAAC), as shown in Fig. 3 (c). The main considerationlied in relativity among each other of adjacent pixels in theimage, therefore, to conduct coding with symbol probabilitymodel established with adjacent pixels would be a moreefficient coding. The reason is in thatnext encoded symbolisemployed probability model which was established byprior fixed amount of data. When a fixed amount isexceeded, theformer data will be deleted and updated, sothat the probability model can be maintained in its neweststatus.

Figure 3. Probability model establishing method.

A.Adjacent DifferenceThe simple pre-processing by eliminating relativity

among pixels is used to enhance coding effect. Bit-planeModel is established with Residuals among pixels. AfterAdjacent Different step, two kinds of information areobtained, one is the difference stored in Residual aftersubtracting each other, and the other one is the sign ofsubtracted value stored in MarkMap. Wherein,ResidualandMarkMap are both Three Dimensional Array, and i axislength is image height, j axis length is image width, k axislength is color type unit, as shown in Fig. 4. The adoptedimage format is a 24 bit BMP file of three primary colors,hence k axis length of bothResidualand MarkMap are 3.

Figure 4. Three dimension model ofMarkMap andResidual.

1) In the algorithm, element k at position 0, stores Bluedata of pixels, element k at position 1, stores Green data of

pixels, element k at position 2, stores Red data of pixels. Hrepresents image Height, W represents image Width.