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.

Adaptive RLE-Huffman Lossless and Diagnostically Lossless Compression for Pharynx and Esophagus Fluoroscopic Images

Abstract: Diagnostic imaging devices such as fluoroscopy produce a vast number of sequential images, ranging from localization images to functional tracking of the contrast agent moving through anatomical structures such as the pharynx and esophagus. In this paper, an effective method for lossless and diagnostically lossless compression of fluoroscopic images is proposed. The two main contributions are: (1) compression through blockbased subtraction matrix division and adaptive Run Length Encoding (RLE), and (2) range conversion to improve the compression performance. The region of coding (RC) – in this case the pharynx and esophagus, is effectively cropped and compressed using customized correlation and the combination of Run-Length Encoding (RLE) and Huffman Coding (HC), to increase compression efficiency. The experimental results show that the proposed method is able to improve the achieved compression ratio by 488% as compared to that of the benchmark traditional methods.

A Lossless Compression Method for Compound Images

Abstract: To transmits the compound images, it needs efficient compression methods because compound images has combination of text, graphics and natural image. The existing compression methods which contain, compound images are insufficient to compress because it contain text and graphics and natural images. This paper proposes a novel coding scheme from the H.264 intraframe coding. To overcome this problem we use two modes of intra coding methods. The first is the residual lossless scalar quantization (RLSQ) mode, where intra-predicted residues are directly quantized and coded without transform. The second is the base colors and index map (BCIM) mode that can be viewed as an adaptive color quantization. That the proposed scheme improves the coding efficiency even at most bit rates for compound images and it has very low complexity and provides visually lossless quality while keeping competitive compression ratios.

Residual Coding of Images for L-Infinity Control under the JPEG 2000 Framework

Abstract: In this paper, the use of residual coding of images is investigated as a method for controlling maximum absolute error (MAE), or L-infinity distortion metric, in lossy image compression based almost 100% on the JPEG 2000 framework. The images are lossy compressed at an open loop bit rate (ROL) using the JPEG 2000 coder, and in order to obtain the residuals, images are decompressed at the encoder side. Uniform quantization of the residuals is used to control the achieved maximum absolute error. The quantized residuals are then coded losslessly using JPEG 2000 with zero levels of wavelet decomposition, which is a direct application of EBCOT (D. Taubman, 2000). A small subset of the largest residuals (outliers) can be optionally coded separately. A comparison of two techniques used for selecting the best combination of open loop, EBCOT, and outlier rates is presented. The images used in this study are bands extracted from hyperspectral data sets, for which the control of MAE is important