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.

Quantity control for JPEG image data compression using fuzzy logic algorithm

Abstract: The JPEG established the first international standard for continuous-tone still image for both gray scale and color. However, since the JPEG's standard was originally designed for general applications, some modifications must be done for specific applications such as in digital still cameras. We propose a new method to control the bit-rate of the JPEG standard using a fuzzy logic algorithm (FLA). A gain factor is determined to derive an appropriate quantization matrix. Several rules have been prepared to calculate the gain factor. We used 18 test images for simulations and the results show that the mean error is -1.5%, standard deviation is 1.5% and the error range from -3% to 1%. Without the FLA, the error range from -41 to 43%, the mean error is -0.11% and the standard deviation is 24.24%. >

A Steganographic Embedding Undetectable by JPEG Compatibility Steganalysis

Abstract: Steganography and steganalysis of digital images is a cat-and-mouse game. In recent work, Fridrich, Goljan and Du introduced a method that is surprisingly accurate at determining if bitmap images that originated as JPEG files have been altered (and even specifying where and how they were altered), even if only a single bit has been changed. However, steganographic embeddings that encode embedded data in the JPEG coefficients are not detectable by their JPEG compatibility steganalysis. This paper describes a steganographic method that encodes the embedded data in the spatial domain, yet cannot be detected by their steganalysis mechanism. Furthermore, we claim that our method can also be used as a steganographic method on files stored in JPEG format. The method described herein uses a novel, topological approach to embedding. The paper also outlines some extensions to the proposed embedding method.

Lossless Image Compression Using Super-Spatial Structure Prediction

Abstract: We recognize that the key challenge in image compression is to efficiently represent and encode high-frequency image structure components, such as edges, patterns, and textures In this work, we develop an efficient lossless image compression scheme called super-spatial structure prediction This super-spatial prediction is motivated by motion prediction in video coding, attempting to find an optimal prediction of structure compo- nents within previously encoded image regions We find that this super-spatial prediction is very efficient for image regions with sig- nificant structure components Our extensive experimental results demonstrate that the proposed scheme is very competitive and even outperforms the state-of-the-art lossless image compression methods

A method to detect JPEG-based double compression

Abstract: Digital multimedia forensics is an emerging field that has important applications in law enforcement, the protection of public safety, and notational security. As a popular image compression standard, the JPEG format is widely adopted; however, the tampering of JPEG images can be easily performed without leaving visible clues, and it is increasingly necessary to develop reliable methods to detect forgery in JPEG images. JPEG double compression is frequently used during image forgery, and it leaves a clue to the manipulation. To detect JPEG double compression, we propose in this paper to extract the neighboring joint density features and marginal density features on the DCT coefficients, and then to apply learning classifiers to the features for detection. Experimental results indicate that the proposed method delivers promising performance in uncovering JPEG-based double compression. In addition, we analyze the relationship among compression quality factor, image complexity, and the performance of our double compression detection algorithm, and demonstrate that a complete evaluation of the detection performance of different algorithms should necessarily include both the image complexity and double compression quality factor.

Lossless wideband audio compression : prediction and transform

Abstract: Lossless Wideband Audio Compression: Prediction and Transform This thesis studies lossless audio compression. In the domain of lossless compression, research takes place on two broad development sections, signal modeling and coding algorithm. The former is concerned with the understanding of the source signal, while coding is the more tightly specified task of efficiently representing a single symbol as a code. The focus of this thesis is the evaluation and the development of signal modeling techniques for lossless compression. Related with the modeling method used to decorrelate a signal, the data compression schemes are generally divided in two categories, predictive modeling and transform-based modeling. In the thesis, all two categories are investigated in depth and handled from the lossless viewpoint. The first contribution of the thesis is an exploration of the general audio compression systems including the lossy compression system. In predictive modeling, the structures of various linear prediction filters are introduced by presenting the fundamental autoregressive modeling. The prediction filters including the approaches to the nonstationary signal modeling and to the adaptive linear prediction filters are explored and evaluated by testing within a prototypical lossless audio compression system. For transform modeling, two well-known subband transform coding methods, Laplacian pyramid and subband coding scheme, are first described, and then the design methods of perfect reconstruction multirate filter banks are studied. Concerning with the modulated lapped orthogonal transform, the efficiency of linear prediction from subband and from fullband is formally compared and empirically examined. Wavelet transform is in depth studied from the various viewpoints in order to find the theoretical relationship between the wavelet and the multirate filter banks. Theoretical and practical aspects of reversible transforms are discussed by introducing the S-transform, S+P transform, and RTS transform. The lifting method is examined as a means to realize the biorthogonal wavelets. Integer lifting scheme with rounding-off method is investigated to construct reversible version of wavelet transforms and its performance is validated by applying to lossless audio compression. Finally, some of the more important results presented in this thesis are summarized with the suggesting directions for future research.