The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS

By Marcelo J. Weinberger, Gadiel Seroussi, Guillermo Sapiro. IEEE Transactions on Image Processing, 2000

1,847 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4cb8320aacbe52db5a3b28db7439c9b5a8dc95fd

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Citations in the corpus

Improved Embedding for Prediction-Based Reversible Watermarking (2011, methodology): “MED is a high-performance predictor already used in JPEG-LS standard [29].”
A Novel Reversible Data Hiding Scheme Based on Two-Dimensional Difference-Histogram Modification (2013, methodology): “Moreover, in addition to DBT, median-edge-detection (MED, see also [36] and [19]) and error energy estimator are used to obtain a more concentrate histogram and to locate the embeddable pixels.”
Efficient Compression of Encrypted Grayscale Images (2010, background): “The Shannon entropy of the residual is used as the criterion, and the results of MED, GAP, and CAI are listed in Table II.”
Introduction to Data Compression (2005, methodology): “For a detailed description of the LOCO-I and JPEG-LS compression algorithm, see “The LOCO-I Lossless Image Compression Algorithm: Principles and Standardization into JPEG-LS,” Hewlett-Packard Laboratories Technical Report HPL-98-193, November 1998 [92].”
High Speed Lossless Image Compression (2015, methodology): “Dedicated lossless image compression methods like jpeg-ls [27] peak at around 25MiB/s, with modifications reaching 75MiB/s on an Intel R © CoreTM i7-920 processor at 2.”
Fast multiplierless approximations of the DCT with the lifting scheme (2001, methodology): “The binDCT-based lossless transforms are compared with two advanced context model-based prediction methods: the HP LOCO-I [51] and CALIC [52].”
Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection (2011, methodology): “For example, the context adaptive predictor median edge detector (MED [26]) is employed in many works [13]–[15]: if , if , otherwise, where , and are the right, lower and diagonal neighbors of (see Fig.”
Local-Prediction-Based Difference Expansion Reversible Watermarking (2014, methodology): “We remind you that MED is also used in JPEG-LS standard [20].”
Learned Image Downscaling for Upscaling Using Content Adaptive Resampler (2019, methodology): “Table 5 reports the average bits-per-pixel (bpp) of the lossless compressed images using the lossless JPEG (JPEG-LS) [60].”
General Framework to Histogram-Shifting-Based Reversible Data Hiding (2013, background): “3(a)), consider the context adaptive predictor MED (median edge detector [34]):”
An overview of JPEG-2000 (2000, methodology): “Although LOCO-I [ 3 ] was ultimately selected as the basis for JPEG-LS, it was recognized that CREW provided a rich set of features”
Lossless generalized-LSB data embedding (2005, background): “Typically, the probability distribution of the prediction error, = s − ˙ s, can be approximated fairly well by a Laplacian distribution with zero mean and a small variance which is correlated with the context d [15, pp. 33],[16], [17].”