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.

Statistical Model of JPEG Noises and Its Application in Quantization Step Estimation

Abstract: In this paper, we present a statistical analysis of JPEG noises, including the quantization noise and the rounding noise during a JPEG compression cycle. The JPEG noises in the first compression cycle have been well studied; however, so far less attention has been paid on the statistical model of JPEG noises in higher compression cycles. Our analysis reveals that the noise distributions in higher compression cycles are different from those in the first compression cycle, and they are dependent on the quantization parameters used between two successive cycles. To demonstrate the benefits from the analysis, we apply the statistical model in JPEG quantization step estimation. We construct a sufficient statistic by exploiting the derived noise distributions, and justify that the statistic has several special properties to reveal the ground-truth quantization step. Experimental results demonstrate that the proposed estimator can uncover JPEG compression history with a satisfactory performance.

A joint source-channel distortion model for JPEG compressed images

Abstract: The need for efficient joint source-channel coding is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical joint source-channel coding schemes is a distortion model to measure the quality of compressed digital multimedia such as images and videos. Unfortunately, models for estimating the distortion due to quantization and channel bit errors in a combined fashion do not appear to be available for practical image or video coding standards. This paper presents a statistical model for estimating the distortion introduced in progressive JPEG compressed images due to both quantization and channel bit errors. Important compression techniques such as Huffman coding, DPCM coding, and run-length coding are included in the model. Examples show that the distortion in terms of peak signal to noise ratio can be predicted within a 2 dB maximum error.

A framework of adaptive steganography resisting JPEG compression and detection

Abstract: Current typical adaptive steganography algorithms take the detection resistant capability into account adequately but usually cannot extract the embedded secret messages correctly when stego images suffer from compression attack. In order to solve this problem, a framework of adaptive steganography resisting JPEG compression and detection is proposed. Utilizing the relationship between Discrete Cosine Transformation DCT coefficients, the domain of messages embedding is determined; for the maximum of the JPEG compression resistant ability, the modifying magnitude of different DCT coefficients caused by messages embedding can be determined; in order to ensure the completely correct extraction of embedded messages after JPEG compression, error correct codes are used to encode the messages to be embedded; on the basis of the current distortion functions, the distortion value of DCT coefficients corresponding to the modifying magnitude in the embedding domain can be calculated; to improve the detection resistant ability of the stego images and realize the minimum distortion embedding, syndrome-trellis codes are used to embed the encoded messages into the DCT coefficients that have a smaller distortion value. Based on the proposed framework, an adaptive steganography algorithm resisting JPEG compression and detection is designed, which utilizes the relationship between coefficients in a DCT block and the means of that in three adjacent DCT blocks. The experimental results that demonstrate the proposed algorithm not only has a good JPEG compression resistant ability but also has a strong detection resistant performance. Comparing with current J-UNIWARD steganography under quality factor 85 of JPEG compression, the extraction error rates without pre-compression decrease from about 50% to nearly 0, while the stego images remain a good detection resistant ability comparing with a typical robust watermarking algorithm, which shows the validity of the proposed framework. Copyright © 2016 John Wiley & Sons, Ltd.

Joint JPEG compression and detection resistant performance enhancement for adaptive steganography using feature regions selection

Abstract: Since it is difficult to acquire a strong JPEG compression resistant ability while achieving a good detection resistant performance for current information hiding algorithms, a JPEG compression and detection resistant adaptive steganography algorithm using feature regions is proposed. Based on the proposed feature region extraction and selection algorithms, the embedding domain robust to JPEG compression and containing less embedding distortion can be obtained. Utilizing the current distortion functions, the distortion value of DCT coefficients in the embedding domain can be calculated. Combined with error correct coding and STCs, the messages are embedded into the cover images with minimum embedding distortion, and can be extracted with high accuracy after JPEG compression, hence, the JPEG compression and detection resistant performance are enhanced at the same time. The experimental results demonstrate that comparing with current J-UNIWARD steganography under quality factor 85 of JPEG compression, the extraction error rates decrease from above 20 % to nearly 0, while the stego images remain a better detection resistant performance comparing with the current JPEG compression and detection resistant adaptive steganography algorithm.

Lossless image compression with multiscale segmentation

Abstract: This paper is concerned with developing a lossless image compression method which employs an optimal amount of segmentation information to exploit spatial redundancies inherent in image data. Multiscale segmentation is obtained using a previously proposed transform which provides a tree-structured segmentation of the image into regions characterized by grayscale homogeneity. In the proposed algorithm we prune the tree to control the size and number of regions thus obtaining a rate-optimal balance between the overhead inherent in coding the segmented data and the coding gain that we derive from it. Another novelty of the proposed approach is that we use an image model comprising separate descriptions of pixels lying near the edges of a region and those lying in the interior. Results show that the proposed algorithm can provide performance comparable to the best available methods and 15-20% better compression when compared with the JPEG lossless compression standard for a wide range of images.