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Run-Length and Dictionary Coding: Information Theory Results (III)
Published in Yun Q. Shi, Huifang Sun, for Multimedia Engineering, 2017
A set of color test images from the JPEG standards committee was used for performance comparison. The luminance component (Y) is of resolution 720 × 576 pixels, while the chrominance components (U and V) are of 360 × 576 pixels. The compression ratios calculated are the combined results for all the three components. The following observations have been reported: When quantized in 8 bits/pixel, the compression ratios vary much less for multilevel images than for bilevel images, and are roughly equal to 2.When quantized with 5 bits/pixel down to 2 bits/pixel, compared with the lossless JPEG, the JBIG achieves an increasingly higher compression ratio, up to a maximum of 29%.When quantized with 6 bits/pixel, JBIG and lossless JPEG achieve similar compression ratios.When quantized with 7–8 bits/pixel, the lossless JPEG achieves a 2.4%–2.6% higher compression ratio than JBIG.
Source coding: compression of video and audio signals
Published in Hervé Benoit, Digital Television, 2002
reference ISO/IEC 10918, and it can be considered as a toolbox for fixed picture compression. We will not describe it in detail, as it is not the object of this book, but we will nevertheless go through its main steps, as it has largely inspired the way in which MPEG works. It should be noted that JPEG compression can be either lossy or lossless (reversible), depending on the application and the desired compression factor. Most common applications use the lossy method, which allows compression factors of more than 10 to be achieved without noticeable picture quality degradation, depending on the picture content. We will only examine the case of lossy JPEG compression, as the coding of I (intra) pictures of MPEG uses the same process; lossless JPEG compression uses a different predictive coding which is not based on DCT, so we will not discuss it here. Lossy JPEG compression can be described in six main steps: 1. Decomposition of the picture into blocks the picture, generally in Y, Cb, Cr format, is divided into elementary blocks of 8 Â 8 pixels (Fig. 3.2), which represents for a 4:2:2 CCIR-601 picture a total number of 6480 luminance (Y) blocks and 3240 blocks for each Cr and Cb component. Each block is made up of 64 numbers ranging from 0 to 255 (when digitized on 8 bits) for luminance, and 128 to 127 for chrominance Cr and Cb.
Lossless HDR Image Compression by Modulo Encoding
Published in IETE Journal of Research, 2023
A. S. Anand Swamy, N Shylashree
Several techniques are available in the literature for fully lossless compression of HDR images. JPEG-LS (ISO/IEC 14495-1) [2] was the lossless compression method published by the JPEG group in 1999 for 16-bit images. The improved version, JPEG-XT (ISO/IEC IS 18477-8) [3] has been published in 2016. Updated JPEG-2000 was published in 2019 (ISO/IEC 15444-1:2019) [4]. All the above JPEG versions can handle images of bit-depth up to 16 bits [5,6]. To accommodate 32-bit images, in [5,6], the logarithm transforms and its inverse are used for additional pre-processing and post-processing whereas in [7], a two-layer approach is adopted. In [8], the floating point intensity values of HDR images are mapped into integers using raw binary formats. Then JPEG-2000 is used for compression and during decompression, integer-to-float reconversion is applied to get back the original HDR image. In [9], the integer formats are obtained by splitting the 32-bit pixel intensity values. Histogram packing techniques are used in [10–12] to generate index images and integer indices which, in turn, utilize lossless JPEG encoding.
A novel resolution independent gradient edge predictor for lossless compression of medical image sequences
Published in International Journal of Computers and Applications, 2021
Urvashi Sharma, Meenakshi Sood, Emjee Puthooran
Avudaiappan compared Huffman coding, Arithmetic coding, Lossless predictive coding, and other various Lossless image compression techniques. Lossless JPEG has been found better compression technique among these entire techniques w.r.t time and compression ratio for images [20]. Sunil and Sharanabasaweshwar presented a new approach by introducing convex-smoothing problems. According to this approach, input image is divided into sub-problems which are solved by using compressed sensing method. Proposed model’s performance is achieved in terms of PSNR and the time taken to perform the reconstruction [21]. Kabir and Monda compared edge-based transformation and entropy coding (ETEC) and prediction-based transformation and entropy coding (PTEC) schemes with the existing lossless compression techniques: ‘joint photographic experts group lossless’ (JPEG-LS), ‘set partitioning in hierarchical trees’ (SPIHT), and ‘differential pulse code modulation’ (DPCM). The ETEC and PTEC algorithms provide better compression than other schemes. PTEC is more suitable than ETEC for compression when both CR and computation time are taken into consideration [22].