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Comparison Fractal Color Image Compression Using YIQ and YUV Color Model

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Comparison Fractal Color Image Compression Using YIQ and YUV Color Model (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 5, 2015 Comparison Fractal Color Image Compression using YIQ and YUV Color Model Eman A. Al-Hilo Rusul Zehwar Physics Department/ College of Education for Girls Physics Department/ College of Education for Girls Kufa University/ Iraq Kufa University/ Iraq Abstract—The principle of fractal image coding is that the image converges to a stable image by iterating the contractive transformations on an arbitrary initial image. This algorithm [ ] [ ] [ ]……………..(1) Partition the image into a number of range blocks and domain blocks. For every range block, the best matching domain block is searched for among all domain blocks by performing a set of transformations on each block. For color image compression, the [ ] [ ] [ ]….…………(2) Fractal coding is applied on different planes of color image independently by treating each plane as gray level image. The coordinate systems used for color image are RGB, YIQ and The YUV color space defines is a term of one luminance YUV. To encode a color image the main idea is to divide the (Y channel) and two chrominance components (UV image into its three different layers or components (RGB, YIQ channels).U represents the color difference between blue and YUV). It is then possible to compress each of these layers signal and luminance (B−Y) and V represents the difference separately, handle each of the layers as an independent image. In between red and luminance (R−Y). RGB values can be this paper the data of the color component (R,G,B) are transformed to YUV color space using equation (3), while transformed two times in two program separately, ones for YIQ YUV values can be converted back to RGB using equation and other for YUV color space. The results show that using (4). [3,4] (YUV) color space is more useful and efficient than using YIQ in fractal image compression, where PSNR increase 0.1% , CR increase 0.31% and ET decrease 2.321%. [ ] [ ] [ ] ,...............…(3) Keywords—Compression image; fractal color image This is the inverse matrix to get the RGB components out compression and iterated function system of the YUV color: I. INTRODUCTION [ ] [ ] [ ] ,...…………(4) Colors are important for human for communicating with the daily encountered objects as well as his species, these colors should be represented formally and numerically within a mathematical formula so it can be projected on device Many researches study the color image compression. Porat computer storage and applications, this mathematical [2001] study the effect of intercolor correlation on color image representation is known as color model that can hold the color compression. He has been compare color compression space, by the means of color’s primary components (Red, techniques that take advantage of this inter color correlation. Green, and Blue) the computer can visualizes what the human Most of these techniques reduce the redundancies between does in hue and lightness. Most of these techniques reduce the color components by transforming the color primaries into a redundancies between color components by transforming the de correlated color space, such as YIQ or YUV. [5] color primaries into a decorrelated color model such as YUV Kasambe and Patel [2007] they have to provide a method and YIQ. [1] that uses genetic algorithms to speed up. The coordinate The YIQ color space has been used in NTSC (National systems used for color image are RGB, YIQ and YUV. The Television System Committee) color TV system and has been time required for the account in fractal image compression employed in USA, Canada, Japan and Korea. The Y stands for with acceptable Image quality for color and gray scale images. luminance components, and I and Q stand for chrominance [2] information for representing color. [2] Al-Hilo [2007] has studied speeding fractal color image RGB values can be transformed to YIQ color space using compression by moment feature includes converting the RGB equation (1), while YIQ values can be converted back to RGB model system to YUV model and minimize of the rang of U using equation (2). [3,4] and V because of most of the image data are concentrated in the range of Y [6] 112 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 5, 2015 II. MATCHING PROCESS (2) The domain must be partitioned into overlapping After the generation of the range and domain pools; one blocks, using specific step size, to generate the takes each range block listed in the range pool and map it with domain blocks (d1,…,dn). They should have the all the domain blocks listed in the domain pool. At each same size of range blocks. mapping instance one determines the mapping coefficients, d) Searching: i.e., scale (s) and offset (o), which are called the IFS (1) Pick up a domain block from the domain pool. coefficient. These parameters (s) and (o) are determined by 2 (2) Perform one of the isometric mappings. applying the least sum X of square errors between ri′ and ri (3) Calculate the scale (s) and offset (o) coefficient according to following equation: [7] using equations (7-8). (4) Apply the following condition to bound the value of ∑ ……………...…….…(5) (s) and offset (o) coefficient: The minimum of occurs when: If s< smin then s=smin and ….…………………....(6) Else if s >smax then s=smax Substituting equation (5) in (6) and using equations (7) one If o< omin then o=omin gets: Else if o >omax then o=omax ∑ ∑ ∑ (5) Quantize the value (s) and offset (o) using ……………….….. (7) ∑ (∑ ) equations (7-8). (6) Compute the approximation error (χ2) using equation (9). ∑ ∑ ∑ ∑ …. ……….......(8) (7) After the computation of IFS code and the sum of ∑ (∑ ) error (χ2) of the matching between the range and the tested domain block., the (χ2) is compared with [∑ ∑ ∑ ∑ 2 registered minimum error (χ min); such that: 2 2 ∑ ]………......(9) If χ < (χ min) then th 2 2 where, di is the i pixel value of the matched domain sopt=is; oopt=io, χ min= χ block. th PosI=domain block index ri is the i pixel value of the range block. n is the number of pixels in each block (i.e. the block size). Sym=symmetry index III. ENCODING TECHNIQUE End if The implementation encoding method could be 2 summarized by the following steps: (8) If χ min < then the search across the domain 1) Load BMP image and put it in (R,G,B) arrays (i.e., blocks is stopped, and the registered domain block three 2D arrays). is considered as the best matched block 2) Convert (R,G,B) arrays to (Y,I,Q) arrays in first (9) Repeat steps (4) to (10) for all symmetry states of program and convert to (Y,U,V) in second program. the tested domain block. (10) Repeat steps (3) to (11) for all the domain blocks 3) Down sample (I and Q) to quarter of its original size in listed in the domain pool. first program and to to (U and V) in second program. (11) The output is the set of IFS parameters 4) For each component (i.e., the original Y, and the down i.e.,i , i , posI, Sym which should be sampled (I,Q) for first program and (the original Y, and the s o down sampled (U,V) for second program) do: registered as a set of fractal coding parameters for the tested range block. a) Establish the range array (12) Repeat steps (1) to (12) for all range blocks listed in b) Down sample the range image to produce the domain the range pool array. (13) Store all IFS mapping parameters as an array of c) Partitioning: record. The length of this array is equal to the (1) The range array must be partitioned into non- number of range blocks in the range pool. overlapping fixed blocks, to generate the range blocks (r1,….,rn). 113 | P a g e www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 6, No. 5, 2015 IV. DECODING TECHNIQUE V. TESTS RESULTS The decoding process by YIQ and YUV model can be This work is carried out in Visual Basic 6.0 version on summarized in the following steps:- Laptop (hp): intel (R) Core (TM) i5-2430M CUP @ 2.40 GHz Processor, 64-bit Operating System and 6.00 GB RAM. To 1) Generating arbitrary domain pool. The domain pool evaluate the performance of the established colour FIC system could be initialized as a blank image or a piece of image by YIQ and YUV model, the proposed system has been tested extracted from any available image. using Lena image (256x256 pixel, 24bits) as test image. These 2) The values of the indices of (is) and (io) for each range tests explore the effect of the following coding parameters on block should be mapped to reconstruct the quantized values of the compression performance parameters of the established the scale (sq) and offset (oq) coefficients. This step is called the system of YIQ color space and YUV color space: dequantization step. A. Maximum and Minimum Scale Tests 3) Choosing the number of possible iterations, and the This set of tests was conducted to study the effect of threshold value of the mean square error (TMSE). At each MinScale, and MaxScale on the compression performance iteration the following steps are performed: parameters of the reconstructed image in YIQ and YUV a) For each range block one determines the coordinates models. Table (1) and (2) show the effects of this test on (xd, yd), of the best matched domain, from the IFS parameters compression performance parameters for YIQ and YUV (posI), in order to extract the domain block (d) from the respectively.
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