PROJECT REPORT Topic Advanced Image Coding By Radhika Veerla Under the guidance of Dr K R Rao TABLE OF ACRONYMS AIC advanced image coding AVC advanced video coding BMP bit map format CABAC DCT context adaptive binary arithmetic coding discrete cosine transform DWT discrete wavelet transform EBCOT embedded block coding with optimized truncation EZW embedded zero tree wavelet coding FRExt fidelity range extensions HD photo high definition photo I frame JM intra frame joint model JPEG joint photographic experts group JPEG LS joint photographic experts group lossless coding JPEG XR joint photographic experts group extended range LBT lapped bi orthogonal transform MSE mean square error PGM portable graymap PNM portable any map PPM portable pixel map PSNR peak signal to noise ratio SSIM structural similarity index VLC variable length coding LIST OF FIGURES Figure 1 The process flow of the AIC encoder and decoder 2 YCbCr sampling formats 4 4 4 4 2 2 and 4 2 0 3 Different prediction modes used for prediction in AIC 4 The specific coding parts of the profiles in H 264 5 Basic coding structure for a macroblock in H 264 AVC 6 Block diagram for CABAC 7 Diagram for zig zag scan and scan line order 8 Block diagram of JPEG encoder and decoder 9 Structure of JPEG 2000 codec 10 Tiling DC level shifting color transformation DWT of each image component 11 Block diagram of JPEG XR encoder and decoder 12 JPEG LS block diagram 13 A causal template of LOCO I Implementation of AIC based on I frame only coding in H 264 and comparison with other still frame image coding standards such as JPEG JPEG 2000 JPEG LS JPEG XR Objective It is proposed to implement advanced image coding AIC based on I frame only coding using JM software and compare the results with other image compression techniques like JPEG JPEG2000 JPEG LS JPEG XR Microsoft HD photo H 263 Iframe coding Coding simulations will be performed on various sets of test images Experimental results are to be measured in terms of bit rate quality PSNR SSIM etc This project considers only main and FRExt high profiles in H 264 AVC I frame coding JPEG using baseline method JPEG 2000 in non scalable but optimal mode Introduction The aim of AIC 1 is to provide better quality with reduced level of complexity while optimizing readability and clarity Though its aim is not to optimize speed it is faster than many of the JPEG 2000 codecs 10 H 264 technology aims to provide good video quality at considerably low bit rates at reasonable level of complexity while providing flexibility to wide range of applications 2 Coding efficiency is further improved in fidelity range extensions FRExt using 8x8 integer transform and works well for more complex visual content JPEG 15 is first still image compression standard which uses 8x8 block based DCT decomposition while JPEG 2000 is a wavelet based compression standard which has improved coding performance over JPEG with additional features like scalability and lossless coding capability has best performance with smooth spatial data JPEG performs well in low complexity applications whereas JPEG 2000 works well in high complexity lower bit rate applications JPEG2000 has rate distortion advantage over JPEG Microsoft HD photo 19 is a new still image compression algorithm for continuous tone photographic images which maintains highest image quality or delivers the most optimal performance JPEG XR 16 extended range a standard for HD photo has high dynamic range image coding and performance as the most desirable feature Its performance is close to JPEG2000 with computational and memory requirements close to JPEG With half the file size of JPEG HD photo delivers lossy compressed image with better perceptual quality than JPEG and lossless compressed image at 2 5 times smaller than the original image JPEG LS 30 lossless is an ISO ITU T standard for lossless coding of still images In addition it also provides support for near lossless compression The main goal of JPEG LS is to deliver a low complexity solution for lossless image coding with the best possible compression efficiency JPEG uses Huffman coding H 264 AVC and AIC systems adopt CABAC encoding technique and HD photo uses reversible integer integer mapping lapped bi orthogonal transform 7 LOCO I low complexity lossless compression for images an algorithm for JPEG LS uses adaptive prediction context modeling and Golomb coding It supports near lossless compression by allowing a fixed maximum sample error Transcoding converts H 263 compression format to that of H 264 and viceversa If the transcoding is done in compression domain it gives better results as the computation only needs to perform on compressed pixels Although the above mentioned compression techniques are developed for different signals they work well for still image compression and hence worthwhile for comparison Different softwares like AIC reference software JM software for H 264 17 JPEG reference software 18 for JPEG HD photo reference software 19 JasPer 20 for JPEG2000 JPEG LS reference software 30 are used for comparison between different codecs The evaluation will be done using bit rates different quality assessment metrics like PSNR SSIM and complexity The following topics are discussed in this report AIC is described in detail as it is implemented and various other codecs used for comparison in brief Different settings used in the softwares and evaluation methodology are discussed The results obtained by evaluating different test images and test images of different sizes using AIC reference software are included Advanced Image Coding Advanced image coding AIC is a still image compression system which combines the algorithms of H 264 and JPEG standard shown in Fig 1 in order to achieve best compression capability in terms of quality factor with less complexity The performance of AIC is close to JPEG 2000 and is lot better than JPEG AIC uses the intra frame block prediction which is originally used in H 264 to reduce the large number of bits to code original input Both AIC and H 264 use CABAC coding while AIC uses position of coefficient matrix as the context 1 It is observed that each block in AIC is modified to get the best compression efficiency possible Fig 1 The process flow of the AIC encoder and decoder 1 Overview The color conversion from RGB to YCbCr allows better compression in channels as chrominance channels have less information content Then each channel is divided into 8x8 blocks for prediction
View Full Document
Unlocking...