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.

Compression of detected SAR imagery with JPEG 2000

Abstract: The wavelet-based JPEG 2000 image compression standard is flexible enough to handle a large number of imagery types in a broad range of applications. One important application is the use of JPEG 2000 to compress imagery collected by remote sensing systems. This general class of imagery is often larger -- in terms of number of pixels) -- than most other classes of imagery. Support for tiling and the embedded, progressively ordered bit stream of JPEG 2000 are very useful in handling very large images. However, the performance of JPEG 2000 on detected SAR (Synthetic Aperture Radar) and other kinds of specular imagery is not as good, from the perspective of visual image quality, as its performance on more 'literal' imagery types. In this paper, we try to characterize the problem by analyzing some statistical and qualitative differences between detected SAR and other more literal remote sensing imagery types. Several image examples are presented to illustrate the differences. JPEG 2000 is very flexible and offers a wider range of options that allow for technology that can be used to optimize the algorithm for a particular imagery type or application. A number of different JPEG 2000 options - - including subband, weighting, trellis-coded quantization (TCQ), and packet decomposition -- are explored for their impact to SAR image quality. Finally, the anatomy of a texture-preserving wavelet compression scheme is presented with very impressive visual results. The demonstration system used for this paper is currently not supported by the JPEG 2000 standard, but it is hoped that with additional research, a variant of the scheme can be fit into the framework of JPEG 2000.

Diagnostically Lossless Compression-2 of Medical Images

Abstract: A method is proposed for compression of medical images. The approach is called DLC2 (Diagnostically Lossless Compression-2). To get lossless effect, we use lossy coding followed by error image coding. An NNVQ (Neural Network Vector Quantizer) is used for lossy compression and Huffman coding is used to code the difference image losslessly. It is an spatial domain technique. No frequency domain transformations are required, this makes the proposed scheme simple and computationally economical. Proposed method can achieve higher compression ratios than existing standard lossless compression techniques and also meets the legal requirement of medical image archiving

Analysis of common lossless compression algorithm

Abstract: The data compression technology is often used in data collection and data transmission systems.Data compression can generally be classified into lossless compression and lossy compression.First,the principle of the commonly used data compression algorithm is introduced,and then this paper focused on the advantages and disadvantages of each method.At last,some problems that need to pay attention to in use are given.

Entropy Feature Based on 2D Gabor Wavelets for JPEG Steganalysis

Abstract: To improve the detection accuracy for adaptive JPEG steganography which constrains embedding changes to image texture regions difficult to model, a new steganalysis feature based on the Shannon entropy of 2-dimensional (2D) Gabor wavelets is proposed. For the proposed feature extraction method, the 2D Gabor wavelets which have certain optimal joint localization properties in spatial domain and in the spatial frequency are employed to capture the image texture characteristics, and then the Shannon entropy values of image filtering coefficients are used as steganalysis feature. First, the decompressed JPEG image is filtered by 2D Gabor wavelets with different scale and orientation parameters. Second, the entropy features are extracted from all the filtered images and then they are merged according to symmetry. Last, the ensemble classifier trained by entropy features is used as the final steganalyzer. The experimental results show that the proposed feature can achieve a competitive performance by comparing with the state-of-the-art steganalysis features for the latest adaptive JPEG steganography algorithms.

Detecting shifted double JPEG compression tampering utilizing both intra-block and inter-block correlations

Abstract: Copy-paste forgery is a very common type of forgery in JPEG images. The tampered patch has always suffered from JPEG compression twice with inconsistent block segmentation. This phenomenon in JPEG image forgeries is called the shifted double JPEG (SDJPEG) compression. Detection of SDJPEG compressed image patches can make crucial contribution to detect and locate the tampered region. However, the existing SDJPEG compression tampering detection methods cannot achieve satisfactory results especially when the tampered region is small. In this paper, an effective SDJPEG compression tampering detection method utilizing both intra-block and inter-block correlations is proposed. Statistical artifacts are left by the SDJPEG compression among the magnitudes of JPEG quantized discrete cosine transform (DCT) coefficients. Firstly, difference 2D arrays, which describe the differences between the magnitudes of neighboring JPEG quantized DCT coefficients on the intrablock and inter-block, are used to enhance the SDJPEG compression artifacts. Then, the thresholding technique is used to deal with these difference 2D arrays for reducing computational cost. After that, co-occurrence matrix is used to model these difference 2D arrays so as to take advantage of second-order statistics. All elements of these co-occurrence matrices are served as features for SDJPEG compression tampering detection. Finally, support vector machine (SVM) classifier is employed to distinguish the SDJPEG compressed image patches from the single JPEG compressed image patches using the developed feature set. Experimental results demonstrate the efficiency of the proposed method.