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Instrument Control and Onboard Data Handling
Published in Shen-En Qian, Hyperspectral Satellites and System Design, 2020
The lossless compression algorithm is an updated version of the Fast, Efficient, Lossless Image Compression System (FELICS) (Howard and Vitter 1994), which is a hierarchical predictive coding method with resolution scaling. In order to improve the performance of image decorrelation and entropy coding of the FELICS, a two-dimensional (2D) interpolation prediction is introduced to decorrelate image data, and an adaptive Golomb-Rice encoder is used to replace the entropy encoder. The compression speed is almost 30 times faster than JPEG-LS or lossless JPEG2000, whereas the compression ratio is only 1% or 5% less than that obtained using JPEG-LS or lossless JPEG2000 (Hihara et al. 2015).
Compression of color images
Published in Sharma Gaurav, Digital Color Imaging Handbook, 2017
Another important compression method is the JPEG-LS standard.22,23 It is a low-complexity compressor aimed at lossless or near-lossless image compression. JPEG-LS is based on pixel prediction and prediction error encoding (a very sophisticated DPCM coder, in essence). Prediction is adaptive, and compression is based on variable length coding.
Lossless Compression
Published in Jerry D. Gibson, The Communications Handbook, 2018
• Instead of using a simple linear predictor, JPEG-LS uses the MED predictor described earlier, which attempts to detect the presence of edges passing through the current pixel and accordingly adjusts prediction. This results in a significant improvement in performance in the prediction step as compared to the old JPEG lossless standard.
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.
An Approach for Diagnostically Lossless Coding of Volumetric Medical Data Based on Wavelet and Just-Noticeable-Distortion Model
Published in IETE Journal of Research, 2023
B. K. Chandrika, P. Aparna, S. Sumam David
VLIC is compared with state-of-the-art codecs such as JPEG-LS [30], JPEG2K [31], JPEG3D [32], and MILC [33] for data set-1. For data set-2, VLIC is compared with JPEG-LS, JPEG2K, DPCM, and HEVC results obtained from [16]. For data set -3, VLIC is compared with JPEG-LS, JPEG2K, JPEG3D, and HEVC results obtained from [17]. JPEG-LS is a near-lossless/lossless compression standard. It is developed for natural images and is developed based on the prediction technique, residual modelling, and context-based coding of the residuals.