Lossless JPEG

About: Lossless JPEG is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 51110 citations. The topic is also known as: Lossless JPEG & .jls.

Reversible inter-frame compression of multispectral images based on a previous-closest-neighbor prediction

Abstract: Previous closest neighbor (PCN) prediction has prevously been proposed for lossless data compression of multispectral images, in order to take advantage of inter-band data correlation. The basic idea to predict the value of the current pixel in the current band on the basis of the best zero-order predictor on the previously coded band, has been applied by extending the set of predictors to those adopted by lossless JPEG. Performances increase more than 5% when passing from the original set of predictors to the extended set.

Model observer optimization of JPEG image compression

Abstract: Recent work applied model observers to predict the effect of JPEG and wavelet image compression on human visual detection of simulated lesions embedded in real structured backgrounds (x-ray coronary angiograms). 1 We extend the use of model observers to perform parameter optimization of image compression in order to maximize visual detection performance at a given compression ratio. A simulated annealing algorithm 2 was used to find the optimal quantization matrix. In each iteration, the 64 quantization parameters of the JPEG algorithm were randomly perturbed (while preserving a fixed compression ratio). Each set of quantization parameters setting was used to compress 400 test images. Model observer performance (Pc) was then obtained for the images that had undergone compression. The simulated annealing algorithm converged (as determined by the annealing schedule) to an optimal quantization matrix. A follow-up human psychophysical study with two naive observers was conducted to compare optimal quantization matrix with respect to the default JPEG quantization matrix. For both human observers visual detection performance improved significantly from the default to the optimized quantization matrix condition. Our results suggest that model observers can be successfully used for task-based performance optimization of image compression algorithms.

Computer image quality selection between JPEG, JPEG 2000 and PDE compression

Abstract: Importance of compression in the field of digital image processing is growing due to the increasing amount and resolution of images. Research on lossy image compression based on partial differential equations (PDEs) is still a subject of ongoing research. One way of determining efficiency of a lossy compression method is to quantify the reconstruction error between the original image and the reconstructed image. The image quality can be evaluated objectively and subjectively. Our experiments using different image quality assessment methods demonstrate that for high compression rates, PDE-based approach does not only give far better results than the widely-used JPEG standard, but can even beat the quality of the highly optimised JPEG 2000 codec.

A Tutorial on Hierarchical Lossless Data Compression

Abstract: Hierarchical lossless data compression is a compression technique that has been shown to effectively compress data in the face of uncertainty concerning a proper probabilistic model for the data. In this technique, one represents a data sequence x using one of three kinds of structures: (1) a tree called a pointer tree, which generates x via a procedure called “subtree copying”; (2) a data flow graph which generates x via a flow of data sequences along its edges; or (3) a contextfree grammar which generates x via parallel substitutions accomplished with the production rules of the grammar. The data sequence is then compressed indirectly via compression of the structure which represents it. This article is a survey of recent advances in the rapidly growing field of hierarchical lossless data compression. In the article, we illustrate how the three distinct structures for representing a data sequence are equivalent, outline a simple method for designing compact structures for re presenting a data sequence, and indicate the level of compression performance that can be obtained by compression of the structure representing a data sequence.

Perceptually lossless coding of medical images - from abstraction to reality

Abstract: This work explores a novel vision model based coding approach to encode medical images at a perceptually lossless quality, within the framework of the JPEG 2000 coding engine. Perceptually lossless encoding offers the best of both worlds, delivering image