Modified advanced image coding Zhengbing Zhang Electronics and Information College, Yangtze University Supervisor: Dr K.R. Rao Electrical Engineering Department, University of Texas at Arlington

Outline 1. Introduction 2. JPEG-Baseline 3. JPEG 2000 4. Advanced Image Coding 5. Modified Advance Image Coding(M-AIC) 6. Simulations 7. Conclusions and Future Work

1. Introduction JPEG[1] has played an important role in image storage and transmission since its development. JPEG provides very good quality of reconstructed images at low or medium compression but it suffers from blocking artifacts at high compression. Several papers [2]~[7] have been published to improve the performance of DCT-based image compression. In his website[8], Bilsen provides an experimental still image compression system known as Advanced Image Coding (AIC) that performs much better than JPEG and close to JPEG-2000[10].

2. JPEG-Baseline (a) Encoder (b) Decoder

3. JPEG 2000 Based on wavelet transform Context Coding Algorithm: EBCOT (Embedded Block Coding with Optimal Truncation) Context-based Arithmetic Entropy Coding This simulation disables tiling and scalable mode Reference software[10]: JasPer v 1.900.1

4. Advanced Image Coding (a) Encoder [8] (b) Decoder [8]

Advanced Image Coding Coefficients encoded in scan-line order It is a still image compression system which is a combination of H.264 and JPEG standards. Features: No sub-sampling- higher quality / compression ratios 9 prediction modes as in H.264 Predicted blocks are predicted from previously decoded blocks Uses DCT to transform 8x8 residual block instead of transform coefficients as in JPEG Employs uniform quantization Uses floating point algorithm Coefficients encoded in scan-line order Makes use of CABAC similar to H.264 with several contexts

5. M-AIC (a) M-AIC Encoder (b) M-AIC Decoder G R Cr Cb Y CC  Mode Select and Store Block Predict mode Y, Cb, Cr Blks +  Pred Blk FDCT Q ZZ Huff A C Q1 IDCT Table Res Res Dec Y DecY DecCb DecCr Predictor ModeEnc (a) M-AIC Encoder B G R Cr Cb Y ICC  Block Predict Y,Cb,Cr Blks + Pred Blk IDCT Q1 IZZ IHuff A D Table Res ModeDec and Store mode DecY DecCb DecCr (b) M-AIC Decoder CC - color conversion, ICC - Inverse CC, ZZ – zig-zag scan, IZZ – inverse ZZ, AAC – adaptive arithmetic coder, AAD – AA decoder.

Color Conversion Y = 0.299R + 0.587G+ 0.114B Cb=-0.169 R - 0.331G +0.5 B Cr= 0.5 R - 0.419G - 0.081 B R=Y+ 1.402Cr G=Y - 0.344Cb-0.714Cr B=Y+ 1.772Cb YCbCr format is 4:4:4. The color conversion method same as in JPEG reference software [9] is used.

Prediction Modes[8] Mode 0: Vertical Mode 1: Horizontal Mode 2: DC Diagonal Down-Left Mode 4: Diagonal Down-Right Mode 5: Vertical-Right Mode 6: Horizontal-Down Mode 7: Vertical-Left Mode 8: Horizontal-Up

Prediction Modes (contd.) Determine only when coding each Y block By full search among the 9 modes minimize the prediction error with Sum of Absolute Difference The selected prediction mode is stored & used for blocks in Y, Cb and Cr. ModeEnc encodes selected prediction modes with a variable length algorithm.

Encode the prediction residual The prediction residual (Res) is transformed into DCT coefficients with floating point DCT. DCT coefficients are uniformly scalar-quantized: same QP for all the DCT coefficients of Y, Cb and Cr. zig-zag scan Encode 64 coefficients of a block with the same algorithm for the AC coefficients in JPEG[1][9]. Use the Huffman table for AC coefficients of chrominances recommended in baseline JPEG [1][9].

File Format stream header : 11 bytes (format flag, version, QP, image width, image height, pixel depth, code size of the compressed modes). stream order: header, code of prediction modes, Huffman codes of Y-Res, Cb-Res and Cr-Res. An adaptive arithmetic coder [12][13]: input byte-by-byte from the compressed stream; output finally compressed result.

M-AIC Codec

M-AIC Codec

6. Simulations Performance comparisons with bit-rate vs PSNR Original and compressed Lena image with different methods

Test images (a) Lena 51251224 (b) Airplane 51251224 (c) Couple 25625624 (d) Peppers 51251224 (e) Splash 51251224 (f) Sailboat 51251224

Performance comparisons with bit-rate vs PSNR (a) Lena (512x512x24) (b) Airplane (512x512x24) (c) Couple (256x256x24) (d) Peppers (512x512x24)

Performance comparisons with bit-rate vs PSNR (contd.) (e) Splash (512x512x24) (f) Sailboat (512x512x24)

Original and compressed Lena image with different methods Original Lena (51251224) (b) AIC: 0.22bpp, PSNR=28.84dB (c) JPEG2000: 0.22bpp, PSNR=29.57dB

Compressed Lena image with different methods(contd.) (d) M-AIC: 0.22bpp, PSNR=29.02dB (e) JPEG: 0.22bpp, PSNR=24.29dB

Compressed Lena image with different methods(contd.) (f) AIC: 0.15bpp, PSNR=27.29dB (g) M-AIC: 0.15bpp, PSNR=27.43dB (h) JPEG: 0.16bpp, PSNR=14.05dB

Conclusions and Future Work M-AIC performs much better than baseline JPEG, close to AIC and JPEG-2000, and a little bit better than AIC at some low bit rate range. Replace the Huffman coder and AAC with CABAC Replace floating point DCT with integer DCT Try more prediction modes

References W. B. Pennebaker and J. L. Mitchell, JPEG still image data compression standard, Van Nostrand Reinhold, New York, 1993. A. Gupta et al., “Modified runlength coding for improved JPEG performance,” Intl. Conf. on Information and Communication Technology,2007, pp. 235 – 237, Dhaka, Bangladesh, March 2007. G. Lakhani, “DCT coefficient prediction for JPEG image coding,” IEEE Int. Conf. Image Processing, 2007, vol. 4, pp. IV-189 – IV-192, Oct. 2007. C. Wang, et al., “An improved JPEG compression algorithm based on sloped-facet model of image segmentation,” Intl. Conf. on Wireless Communications, Networking and Mobile Computing, 2007, WiCom 2007, pp. 2893 – 2896, Sept. 2007. K. Lee, D.S. Kim, and T. Kim, “Regression-based prediction for blocking artifact reduction in JPEG-compressed images,” IEEE Trans. Image Processing, Vol. 14, pp. 36 – 48, Jan. 2005. E. Yang and L. Wang, “Joint optimization of run-length coding, Huffman coding and quantization table with complete baseline JPEG compatibility,” IEEE Int. Conf. Image Processing, 2007, vol. 3, pp.III-181 – III-184, Oct. 2007. J. Huang and S. Liu, “Block predictive transform coding of still images,” in Proc. IEEE ICASSP-94, vol. 5, pp.III-181 – III-184, April 1994. AIC website: http://www.bilsen.com/aic/ JPEG reference software website: ftp://ftp.simtel.net/pub/simtelnet/msdos/graphics/jpegsr6.zip JPEG 2000 reference software: “JasPer version 1.900.1” on website: http://www.ece.uvic.ca/~mdadams/jasper/ J. Ostermann et al., “Video coding with H.264/AVC: tools, performance, and complexity,” IEEE Circuits and Systems Magazine, vol. 4, issue 1, pp. 7-28, first quarter 2004. I. H. Witten, R. M. Neal, and J. G. Cleary, “Arithmetic coding for data compression,” Communications of the ACM, vol. 30, pp. 520-540, June 1987. Adaptive arithmetic coding source code: http://www.cipr.rpi.edu/~wheeler/ac/ Y-W. Chang and Y-Y. Chen, “Novel artifact removal algorithm in the discreste cosine transform domain,” JEI, vol. 17, pp.013012-1—013012-12, Jan.-Mar. 2008.

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