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.

A data hiding method for JPEG 2000 coded images using modulo arithmetic

Abstract: This paper proposes a novel data hiding method for JPEG 2000 coded images that embed multi-level information into quantized discrete wavelet transformed coefficients. Since the proposed method hides information represented as an integer to a transformed coefficient rounded to an integer, a JPEG 2000 code-stream conveying data keeps its standard JPEG 2000 code-stream structure. The proposed method is able to extract hidden data without memorizing embedding positions; it thus chooses transformed coefficients to which data is hidden freely. This characteristic makes the proposed method suitable for hiding data to a JPEG 2000 coded image with consideration of Region of Interest (ROI) coding that is a major feature. Simulation results show the effectiveness of the proposed method. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(7): 37– 46, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20286

An asymmetric lossless image compression technique

Abstract: While there exist many asymmetric techniques for the lossy compression of image data, most techniques reported for lossless compression of image data have been symmetric. In this paper we present a new lossless compression technique that is well suited for asymmetric applications. It gives superior performance compared to other lossless compression techniques reported in the literature. Hence, it can also potentially be adapted for use in symmetric applications that require high compression ratios.

Joint Compression and Watermarking Using Variable-Rate Quantization and its Applications to JPEG

Abstract: In digital watermarking, one embeds a watermark into a covertext, in such a way that the resulting watermarked signal is robust to a certain distortion caused by either standard data processing in a friendly environment or malicious attacks in an unfriendly environment. In addition to the robustness, there are two other conflicting requirements a good watermarking system should meet: one is referred as perceptual quality, that is, the distortion incurred to the original signal should be small; and the other is payload, the amount of information embedded (embedding rate) should be as high as possible. To a large extent, digital watermarking is a science and/or art aiming to design watermarking systems meeting these three conflicting requirements. As watermarked signals are highly desired to be compressed in real world applications, we have looked into the design and analysis of joint watermarking and compression (JWC) systems to achieve efficient tradeoffs among the embedding rate, compression rate, distortion and robustness. Using variable-rate scalar quantization, an optimum encoding and decoding scheme for JWC systems is designed and analyzed to maximize the robustness in the presence of additive Gaussian attacks under constraints on both compression distortion and composite rate. Simulation results show that in comparison with the previous work of designing JWC systems using fixed-rate scalar quantization, optimum JWC systems using variable-rate scalar quantization can achieve better performance in the distortion-to-noise ratio region of practical interest. Inspired by the good performance of JWC systems, we then investigate its applications in image compression. We look into the design of a joint image compression and blind watermarking system to maximize the compression rate-distortion performance while maintaining baseline JPEG decoder compatibility and satisfying the additional constraints imposed by watermarking. Two watermarking embedding schemes, odd-even watermarking (OEW) and zero-nonzero watermarking (ZNW), have been proposed for the robustness to a class of standard JPEG recompression attacks. To maximize the compression performance, two corresponding alternating algorithms have been developed to jointly optimize run-length coding, Huffman coding and quantization table selection subject to the additional constraints imposed by OEW and ZNW respectively. Both of two algorithms have been demonstrated to have better compression performance than the DQW and DEW algorithms developed in the recent literature. Compared with OEW scheme, the ZNW embedding method sacrifices some payload but earns more robustness against other types of attacks. In particular, the zero-nonzero watermarking scheme can survive a class

Multithreshold progressive image sharing with compact shadows

Abstract: We propose a multithreshold progressive reconstruction method. The image is encoded three times using Joint Photographic Experts Group (JPEG): first with a low-quality factor, then with a medium-quality factor, and last with a high-quality factor. Huffman coding is employed to encode the difference between the important image and the high-quality JPEG decompressed image. The three JPEG codes and the Huffman code are shared, respectively, ac- cording to four prespecified thresholds. The n-generated equally im- portant shadows can be stored or transmitted using n channels in parallel. Cooperation among these generated shadows can progres- sively reconstruct the important image. The reconstructed image is loss-free when the number of collected shadows reaches the largest threshold. Each shadow is very compact and so can be hidden suc- cessfully in the JPEG codes of cover images to reduce the probabil- ity of being attacked when transmitted in an unfriendly environment. Comparisons with other image sharing methods are made. The con- tributions, such as easiness to apply to scalable Moving Picture Ex- perts Group (MPEG) video transmission or resistance to differential attack, are also included. © 2010 SPIE and IS&T.

An improved lossless image compression algorithm LOCO-R

Abstract: This paper presents a state-of-the-art implementation of lossless image compression algorithm LOCO-R, which is based on the LOCO-I (low complexity lossless compression for images) algorithm developed by weinberger, Seroussi and Sapiro, with modifications and betterment, the algorithm reduces obviously the implementation complexity. Experiments illustrate that this algorithm is better than Rice Compression typically by around 15 percent.