Comparative study of various still image coding techniques. Harish Bhandiwad 1000579432 EE5359 Multimedia Processing

Why Do We Need Compression? Requirements may outstrip the anticipated increase of storage space and bandwidth [11] For data storage and data transmission DVD Video conference Printer The bit rate of uncompressed digital cinema data exceeds 1 Gbps

Image Compression (Bandwidth Compression vs. Bit Rate Reduction) Reduction of the number of bits needed to represent a given image or its information Image compression exploits the fact that all images are not equally likely Exploits energy gaps in signal

Lossless or Lossy Compression Lossless compression [12] There is no information loss, and the image can be reconstructed exactly the same as the original Applications: Medical imagery, Archiving Lossy compression [13] Information loss is tolerable Many-to-1 mapping in compression eg. quantization Applications: commercial distribution (DVD) and rate constrained environment where lossless methods cannot provide enough compression ratio

Standards JPEG JPEG-LS JPEG2000 JPEG-XR MPEG4-VTC AIC

JPEG Encoder and Decoder [1]

JPEG-Baseline JPEG2000 8x8 block based DCT Scalar quantization Zig-zag scanning Different quantization tables for luminance and chrominance components Huffman coding JPEG2000 Relies on wavelet transform EBCOT scheme for coding wavelet coefficients Adaptive context-based binary arithmetic coding This project disables tiling and scalable mode for comparison as they adversely affect rate-distortion performance

JPEG-LS JPEG-LS is ISO/ITU-T standard for lossless coding of still images based on adaptive prediction, context modeling and Golomb coding [2] does not provide support for scalability, error resilience or any such functionality

JPEG-XR encoder and decoder Reversible int-int mapping LBT Scalar quantization Quantization tables 8x8 blocks VLC Encoding Adaptive VLC table switching Original image Block based encoder Coded image Reversible int-int mapping inverse LBT Scalar Dequantization VLC Decoding Quantization tables Adaptive VLC table switching Original image Coded image

JPEG-XR JPEG XR , a coded file format designed mainly for storage of continuous-tone photographic content supports wide range of color formats including n-channel encodings using fixed and floating point numerical representations, bit depth varieties Uses block-based image coder similar to traditional image-coding paradigm: color conversion, transform, coefficient scanning, scalar quantization and entropy coding [14] Uses lapped bi-orthogonal transform (LBT) as its decorrelation engine which supports both lossy and lossless compression [14]

MPEG-4 Visual Texture Coding Used in MPEG-4 standard to compress the texture information in photo realistic 3D models Based on the discrete wavelet transform (DWT), scalar quantization, zero-tree coding and arithmetic coding [7] MPEG-4 VTC supports SNR scalability through the use of different quantization strategies: single quantization, multiple quantization and bi-level quantization [7]

Advanced Image Coding (a) Encoder [3] (b) Decoder [3]

Advanced Image Coding It is a still image compression system which is a combination of H.264 and JPEG standards [3]. Features: No sub-sampling- higher quality / compression ratios 9 prediction modes as in H.264 Predicted blocks are predicted from previously decoded blocks Uses integer DCT to transform 8x8 residual block instead of transform coefficients as in JPEG Employs uniform quantization Uses floating point algorithm Coefficients transmitted in scan-line order Makes use of CABAC similar to H.264 with several contexts

Evaluation Methodology for Comparison Image Quality Measures Criteria used to evaluate compression quality Two types of quality measures Objective quality measure- PSNR, MSE Structural quality measure- SSIM MSE and PSNR for a NxM pixel image are defined as where x is the original image and y is the reconstructed image. M and N are the width and height of an image and ‘L’ is the maximum pixel value in the NxM pixel image. -----(1) -----(2)

Structural Similarity Method This method emphasizes that the Human Visual System (HVS) is highly adapted to extract structural information from visual scenes. Therefore, structural similarity measurement should provide a good approximation to perceptual image quality [8]. The SSIM index is defined as a product of luminance, contrast and structural comparison functions [8]. where > 0, > 0 and > 0 are parameters used to adjust the relative importance of the three components where μ is the mean intensity, and σ is the standard deviation as a round estimate of the signal contrast. C1 and C2 are constants. M is the numbers of samples in the quality map.

References [1]G. K. Wallace, “The JPEG still picture compression standard,” Communication of the ACM, vol. 34, pp. 31-44, April 1991 [2] http://en.wikipedia.org/wiki/Lossless_JPEG [3] AIC website: http://www.bilsen.com/aic/ [4] P. Topiwala, “Comparative study of JPEG2000 and H.264/AVC FRExt I-frame coding on high definition video sequences,” Proc. SPIE Int’l Symposium, Digital Image Processing, Vol 5909, 59090V, San Diego, Aug. 2005. [5] R. Veerla, Z. Zhang and K.R. Rao, Advanced image coding and its comparison with various still image codecs, 2008 [6] T. Tran, L.Liu and P. Topiwala, “Performance comparison of leading image codecs: H.264/AVC intra, JPEG 2000, and Microsoft HD photo,” Proc. SPIE Int’l Symposium, Digital Image Processing, Vol. 6696, 66960B, San Diego, Sept. 2007 [7] I. Moccagatta, H. Chen, “MPEG-4 visual texture coding: more than just compression”, Rockwell Science Center. 1999 [8] Z. Wang and A. C. Bovik, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Processing, vol. 3, pp. 600 – 612, Apr. 2004 [9] http://en.wikipedia.org/wiki/JPEG [10] http://en.wikipedia.org/wiki/JPEG_2000 [11] http://en.wikipedia.org/wiki/Image_compression [12] http://en.wikipedia.org/wiki/Lossless_compression [13] http://en.wikipedia.org/wiki/Lossy_compression [14] http://en.wikipedia.org/wiki/HD_Photo [15] G. J. Sullivan, “ ISO/IEC 29199-2 (JpegDI part 2 JPEG XR image coding – Specification),” ISO/IEC JTC 1/SC 129/WG1 N 4492, Dec 2007