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.

ROI-based Watermarking Scheme for JPEG 2000

Abstract: A new region of interest (ROI)-based watermarking method for JPEG 2000 is presented. The watermark is embedded into the host image based on the characteristics of the ROI to protect rights to the images. This scheme integrates the watermarking process with JPEG 2000 compression procedures. Experimental results have demonstrated that the proposed watermark technique successfully survives JPEG 2000 compression, progressive transmission, and principal attacks.

A method for fusing a pair of images in the JPEG domain

Abstract: We present an efficient algorithm for fusing a pair of long- and short-exposure images that work in the JPEG domain. The algorithm uses the spatial frequency analysis provided by the discrete cosine transform within JPEG to combine the uniform regions of the long-exposure image with the detailed regions of the short-exposure image, thereby reducing noise while providing sharp details. Two additional features of the algorithm enable its implementation at low cost, and in real time, on a digital camera: the camera's response between exposures is equalized with a look-up table implementing a parametric sigmoidal function; and image fusion is performed by selective overwriting during the JPEG file save operation. The algorithm requires no more than a single JPEG macro-block of the short-exposure image to be maintained in RAM at any one time, and needs only a single pass over both long- and short-exposure images. The performance of the algorithm is demonstrated with examples of image stabilization and high dynamic range image acquisition.

Compression of CT Images using Contextual Vector Quantization with Simulated Annealing for Telemedicine Application

Abstract: The role of compression is vital in telemedicine for the storage and transmission of medical images. This work is based on Contextual Vector Quantization (CVQ) compression algorithm with codebook optimization by Simulated Annealing (SA) for the compression of CT images. The region of interest (foreground) and background are separated initially by region growing algorithm. The region of interest is encoded with low compression ratio and high bit rate; the background region is encoded with high compression ratio and low bit rate. The codebook generated from foreground and background is merged, optimized by simulated annealing algorithm. The performance of CVQ-SA algorithm was validated in terms of metrics like Peak to Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR), the result was superior when compared with classical VQ, CVQ, JPEG lossless and JPEG lossy algorithms. The algorithms are developed in Matlab 2010a and tested on real-time abdomen CT datasets. The quality of reconstructed image was also validated by metrics like Structural Content (SC), Normalized Absolute Error (NAE), Normalized Cross Correlation (NCC) and statistical analysis was performed by Mann Whitney U Test. The outcome of this work will be an aid in the field of telemedicine for the transfer of medical images.

Fundamental limits of almost lossless analog compression

Abstract: In Shannon theory, lossless source coding deals with the optimal compression of discrete sources. Compressed sensing is a lossless coding strategy for analog sources by means of multiplication by real-valued matrices. In this paper we study almost lossless analog compression for analog memoryless sources in an information-theoretic framework, in which the compressor is not constrained to linear transformations but it satisfies various regularity conditions such as Lipschitz continuity. The fundamental limit is shown to be the information dimension proposed by Renyi in 1959.