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Modified advanced image coding

Modified advanced image coding. Electronics and Information College, Yangtze University Supervisor: Dr K.R. Rao Electrical Engineering Department, University of Texas at Arlington. Zhengbing Zhang. Outline. 1. Introduction 2. JPEG-Baseline 3. JPEG 2000 4. Advanced Image Coding

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Modified advanced image coding

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

  2. 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

  3. 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].

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

  5. 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

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

  7. Advanced Image Coding 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

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

  9. 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.

  10. Prediction Modes[8] Mode 0: Vertical Mode 1: Horizontal Mode 2: DC Mode 3: Diagonal Down-Left Mode 4: Diagonal Down-Right Mode 5: Vertical-Right Mode 6: Horizontal-Down Mode 7: Vertical-Left Mode 8: Horizontal-Up

  11. 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.

  12. 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].

  13. 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.

  14. M-AIC Codec

  15. M-AIC Codec

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

  17. 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

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

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

  20. 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

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

  22. 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

  23. 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

  24. 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.

  25. Thank you !

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