An Effective Compression Technique for HSL Color Model

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1 An Effective Compression Technique for HSL Color Model Noura.A.Semary Faculty of Computers and Information Menoufia University Shebeen ElKom Egypt Mohiy.M.Hadhoud Faculty of Computers and Information Menoufia University Shebeen ElKom Egypt Alaa.M.Abbas Faculty of Electronic Engineering Menoufia University Monouf Menoufia-Egypt Abstract It's a fact that nonlinear color models like Hue-Saturation -Value/ Brightness/ Luminance/ Intensity (HSV/ HSB/ HSL/ HSI) have special feature for each channel. So in this paper we propose a new hybrid compression system that deals with each channel with a suitable compression technique to obtain encoded images with less size and high decoding quality than the traditional encoding methods. Keywords- HSL; HSI; Compression; Encoding; I. INTRODUCTION Most common image encoding techniques like Jpeg, Jpeg, EZW, and more deal with the image channels (Usually YCbCr channels) similarly. A color model like (Red, Green, Blue) RGB, has a linear relation between its three channels, so its normal to encode the three channels of any color image in RGB in the same way. But with a nonlinear color model like HSL family or YCbCr where luminance channel is separated from chrominance channels, each channel has its own features, so it's better to deal with each channel separately. HSL color model is used in our research due to its definition that based on the human perception of color []. As a brief description for HSL model (figure ) features, the Hue channel presents the color ranges as we can name (Red, Green, Yellow, etc).from table () and table (), the range of hue values changes from o ( red) to 36 o (red) passing throw rainbow colors. Saturation value affect the purity of the colors and it differs from gray ( value) to full saturated/pure color ( value). Finally Luminance means the amount of light in color. So it differs from black to white passing throw the dark values and light values of the color. Figure (3) shows a colored image 'Colored Shapes' with its H, S, L channels fig (4). As shown in the figure the L channel of HSL model is the same the gray version of the image or the image intensity not the Luna component. There is no famous encoding technique based on HSV or HSL color model because any data loss in H channel will return a different color in the overall color image. The proposed system is a hybrid compression system that deals with each channel with a suitable compression technique. This paper is organized as follows: Section presents the proposed encoding system with brief explanation of HSL model features. Section 3 presents the first encoding technique "Hue Encoding Technique" in details; Section 4 presents the second encoding technique "Saturation Estimation"; Section 5 presents the results and the discussions, finally the conclusion of the research is presented in section 6. II. PROPOSED ENCODER SYSTEM The proposed system (figure ) tends to be very lossy compression technique with subjective quality preservation, or a perceptually lossless compression methodology []. There are three basic encoding techniques will be used; Object Compression Encoder (OCE), Minimum Color Difference (MCD), and Jpeg, for encoding H, S, and L respectively. Detailed description for these algorithms will be presented in next sections. HSL color model is used in the proposed system due to its compatibility with human intuition and unrelated chrominance channels. HSL image H S L Color Correction, Segmentation, Median Filters OCE RGB image Color correction, Transformation MCD JPEG Figure (): The proposed System Blocks Reference Number: W-5 9

2 White Green Yellow 6 Cyan 8 Red Blue 4 Magneta 3 Black Figure (): A double cone of HSI/HLS Color Model Table (): Hue an d luminance degradations Change in Lum/Bright Change in Hue Hue =, Saturation = Sat=, Brightness= L θ H H S S There are four named colors we are concerned with, Black, White, Gray and Red. According to the features of HSL model, we conclude the following rules: ) Any gray color, S =, H=don't care. ) Black colors has L =, regardless what the values of Saturation and Hue. 3) White colors, L=, and S=, there is no effect for Hue channel. 4) Both Hue bar limits (, ) are red. So, the correction procedure in order will be as follows: For some value of threshold (α) Black: V I < α : {let S =, H=} White: V I > - α : {let S =, H=} Gray: V S< α : {let S =, H=} Red: V H< α & H>-α : {let H = } Figure (4) presents the original H, S, and L while fig (5) shows the corrected H channel after correcting 5378 pixels. As notices the correction leads to merge black, white, and red pixels to one hue value=, what eliminates the number of objects (wrong objects) in H channel. Table (): Saturation degradation HSI saturation change H =, L =,.5,.5 Figure (3): Original image 'Colored Shape' 64*48 px Size: 9. kb HSV : Hue HSV : Saturation HSV : Brightness Figure (4): Hue, Saturation, and Lightness III. HUE CHANNEL COMPRESSION TECHNIQUE The Hue bar presented in table () has 56 values ( o -36 o in the model and - as normalized values) can be represented by less number of values without image distortion. Most images have limited number of near hue values. Due to this notes, enhancement stages should precede the compression stage. These enhancements are Color Correction, Segmentation, and Median Filtering. A. Color Correction stage: In many images Hue and Saturation changes don't affect the overall color because of the Lightness channel effect like white and black areas. In this paper we propose a new color correction methodology based on the linguistic color definitions in the RGB color images. The idea based on correcting both Hue and Saturation channels according to the colors appear in the color image without any change in their RGB values. The objective of the correction stage is to minimize the number of objects in the H channel to be processed faster and more efficient with OCE presented by [,3]. Hue Before Hue After update: 5378 Figure (5): Hue before and after correction 3 x 5 B. Segmentation stage.5 K mean technique is used for segmentation purpose, for computational time acceleration, we have used the.5 acceleration algorithm proposed by C.Elkan [4]. Fig ( 6, 7) show the segmented 'Color Shapes' image with k=4. The segmented H channel has no noticeable effect on the final RGB image as seen. Median filter can be used before compression to eliminate noise pixels. Figure (8) presents the colored image with using the segmented H instead of the original one..5 Reference Number: W-5 3

3 Peak Signal to Noise Ration (PSNR) and Mean Square Error (MSE) quality measures are used to evaluate the correction and segmentation results with values PSNR=54.543, MSE=.85. PSNR = = mn Max.log MSE () m n [ I( i, j) K( i, j) ] MSE () i= j= The PSNR and MSE are calculated using equations () and () [5]. Two measures for PSNR are calculated in this paper; Y_PSNR for PSNR calculated on Y channel of YCbCr of the image. And A_PSNR for the Average PSNR of the three channels of YCbCr version of the image. segmented to 4 A. Image Preparation: converts the image into D matrix, which consists of the pixels values (colors) of that image. B. Object Extraction: Applies the (Minimum Number of Pixels) MNP method which extracts the pixels of the current object with respect only to external using chain code. C. Object Minimization: removes the repeated pixels in horizontal, vertical and diagonal edges respectively, and then removes the intersection pixels between vertical and horizontal edges, and between horizontal and diagonal edges. D. Vectoring: Thus, the encoder arranges the reset bounding pixels according to the arrangement that was recorded in object extraction phase giving W code for each pixel. E. Differential Encoding: the number of bits that are required to encode the W values is reduced. F. Huffman Encoding: to compress the result lossless. G. Frame building: The MNP decoder has a general structure that defines the format of total fields in output bit-stream frame. Figure (6): Segmentation results 4 clusters After Median filter 5 5 Cluster no: Cluster no:87 Cluster no3:4 Cluster no4:68 Figure (9): OCE algorithm Figure () shows the resulted boundary pixels after using OCE. The final H channel can be encoded to.4 kb, with compression ratio=.4 Figure (7): Segmented image Figure (): Object compression results with size of.68 kb Figure (8): (Left) Original, (Right) using segmented H. C. Object Compression stage The object compression technique proposed by [,3], presented in fig (9) consists of the following basic blocks: IV. SATURATION ESTIMATION In this stage we propose a new encoding technique for S channel called Minimum Color Difference ( MCD). The proposed algorithm based on the equations of RGB/HSL conversion [6,7]. Reference Number: W-5 3

4 From the equations of RGB to HSL conversion Eq.3; S = - M/L (3) Where M = Min(R, G, B). We assumed that for Hue sectors, the Red, Green and Blue values will be as follows: Original S Estimated S Difference Red Green Blue RG sector L L Minimum GB sector Minimum L L BR sector L Minimum L By substitution in equation (3), we obtain S RG = - B/L (4) Figure (): (Left) Original S, (center) Decoded S, (Right) Difference Let : S GB = - R/L (5) S BR = - G/L M B = L-B in RG sector, (6) M R = L-B in GB sector, and M G = L-G in BR sector. So, MCD can be calculated as: MCD = M B U M R U M G (7) We found the ranges of MCD are less than image saturation by more than 5% so it's better to process MCD rather than Saturation fig (). At the decoder side, R, G, and B can be recalculated using equation 3. The resulted MCD has low contrasted values, so transformation to DCT can be used for compression purpose. DCT compression is applied on the MCD by eliminating frequencies less than some threshold. In this example. is the selected threshold, and we obtain.3 compression ratio after using zigzag and run length coding. The decoded S presented in fig (). The final decoded color image is presented in fig (3), with quality measures calculated Figure (3): A_PSNR= , Y_PSNR= , MSE=3.344 V. RESULTS AND DISCUSSIONS Jpeg codec is used for encoding L channel, using "Jpeg Compressor Software " produced by Anything 3D Corp. Compared to using jpeg with the RGB and YCbCr images, using HSL model instead, can gives good results with less size. A comparison was made between our system result and using jpeg,presented by C.Gonzalez [6]. The same jpeg quality was used for the test. Figure 4 presents the comparison results. Figure 4.a presents the result of jpeg using YCbCr color model (the default color model), figure 4.b presents the jpeg result using HSL color model (usually unused), and figure 4.c presents our HSL encoding system results. A zoomed part of the figures are presented in (D). Compression ratio, MSE, and PSNR were measured and attached to the figures. As notices, our system result is more accepted subjectively and objectively. More images were tested, 'Tiger ( 45 kb)' and 'Young Lady (489 kb)'. Figures ( 5, 6) present the original and the decoded images, with quality measures, PSNR, MSE and Compression Ration CR. VI. CONCLUSION In this paper we proposed a new color image compression schema based on HSL color model. The proposed algorithm based on compressing each channel using a suitable compression algorithm. Hue channel is encoded using object compression algorithm after corrected by our proposal correction algorithm, Lightness channel is encoded using standard jpeg algorithm. And finally the Saturation channel encoded using a proposed estimation method using Minimum Color Difference ( MCD). The proposed compression method gives very high decoding quality and less encoding size compared to other encoding techniques. Figure (): S vs MCD Reference Number: W-5 3

5 (A) Jpeg with YCbCr => Cr:.57, PSNR =3.4, MSE=38.79 (B) Jpeg with HSL => Cr:., PSNR =33.5, MSE=8.49 (C) Proposed System => Cr:.78, PSNR =39.534, MSE=3.785 Figure (6) PSNR= , MSE=3.654, CR :.356 REFERENCES [] K.N.plataniotis and A.N.Venetsanopoulos Color image processing and application, ISBN Springer Verlag Berlin Heidelberg New York [] Fahd M.Al-aghbari "Objects Compression In Multimedia Systems". Master Thesis, Faculty of Computers and Information, Minufiya University 7 [3] Fahd M.A, Kamel A.Mostafa, Nabil.A.Ismail, and Mohiy M. Hadhoud, An Efficient Objects Compression Method by Minimizing Objects Number of an Image, Radio Science Conference, 7. NRSC 7. National [4] C.Elkan, Using the triangle inequality to accelerate k-means, In Proceeding of the th ICML, Washingt, 3. pp [5] Khalid Sayood.. Introduction to Data Compression (nd Ed.). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA [6] Rafael C.Gonzalez,Richard E.Woods and Steven L.Eddins, Book, "Digital Image Processing Using Matlab", Pearson Prentice Hall, 4. [7] Konstantinos N. Plataniotis and Anastasios N. Venetsanopoulos.. Color Image Processing and Applications. Springer-Verlag New York, Inc., New York, NY, USA (D) Zoomed part Figure (4) Comparison between Jpeg with YCBCR (A), jpeg with HSL (B), and Our HSL encoding system (C). (D) Zoomed Figure (5): PSNR= , MSE = 7.66, CR:.99 Reference Number: W-5 33