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.

Digital Forensic of JPEG Images

Abstract: Since JPEG is the de facto image format adopted in most of the digital cameras and image editing software, tampered image will be often a recompressed JPEG image. As JPEG works on 8 by 8 block cosine transform most of the tampering correlation inherited by tampered image may get destroyed, making forgery detection difficult thus it is common practice followed by forger to hide traces of resampling & splicing. JPEG forgery detection techniques try to identify inconsistencies in the artifacts introduced in image due to 8 by 8 block DCT transform. The original image on which forgery is created may be compressed or uncompressed image, similarly area pasted may belong to compressed or uncompressed image. Since both will be having different compression history JPEG forgery detection techniques try to identify the difference in compression history which may be in the form of shifting of DCT block alignment, difference in primary quantization table or ghost detection.

Application of permutations to lossless compression of multispectral thematic mapper images

Abstract: The goal of data compression is to find shorter representations for any given data. In a data storage application, this is done in order to save storage space on an auxiliary device or, in the case of a communication scenario, to increase channel throughput. Because remotely sensed data require tremendous amounts of transmission and storage space, it is essential to find good algorithms that utilize the spatial and spectral characteristics of these data to compress them. A new technique is presented that uses a spectral and spatial correlation to create orderly data for the compression of multispectral remote sensing data, such as those acquired by the Landsat Thematic Mapper (TM) sensor system. The method described simply compresses one of the bands using the standard Joint Photographic Expert Group (JPEG) compression, and then orders the next band’s data with respect to the previous sorting permutation. Then, the move-to-front coding technique is used to lower the source entropy before actually encoding the data. Owing to the correlation between visible bands of TM images, it was observed that this method yields tremendous gain on these bands (on an average 0.3 to 0.5 bits/pixel compared with lossless JPEG) and can be successfully used for multispectral images where the spectral distances between bands are close.

Gaussian Hermite polynomial based lossless medical image compression

Abstract: The role of compression is inevitable in the storage and transmission of medical images. The polynomial based image compression is proposed in this work for the compression of abdomen CT medical images. The input images are preprocessed by min–max normalization; the pixels are scanned and subjected to polynomial approximation. The polynomial approximated coefficients are subjected to llyods quantization and encoded by arithmetic coder. The medical image compression using Gaussian Hermite polynomial gives superior results when compared with the legendre polynomial based image compression and JPEG lossless compression techniques in terms of Peak to signal noise ratio (PSNR), Mean square Error (MSE) and other picture quality metrics. The algorithms are tested on real-time DICOM abdomen CT image and can be used for data transfer in teleradiology application.

Efficient Level and Zero Coding Methods for H.264/AVC Lossless Intra Coding

Abstract: Since H.264/AVC was designed mainly for lossy video coding, the entropy coding methods in H.264/AVC are not appropriate for lossless video coding. Based on statistical differences of residual data in lossy and lossless coding, we develop efficient level and zero coding methods. Therefore, we design an improved context-based adaptive variable length coding (CAVLC) scheme for lossless intra coding by modifying the relative entropy coding parts in H.264/AVC. Experimental results show that the proposed method provides approximately 6.8% bit saving, compared with the H.264/AVC FRExt high profile.