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 Benford-Fourier JPEG compression detector

Abstract: Intrinsic statistical properties of natural uncompressed images can be used in image forensics for detecting traces of previous processing operations. In this paper, we extend the recent theoretical analysis of Benford-Fourier coefficients and propose a novel forensic detector of JPEG compression traces in images stored in an uncompressed format. The classification is based on a binary hypothesis test for which we can derive theoretically the confidence intervals, thus avoiding any training phase. Experiments on real images and comparisons with state-of-art techniques show that the proposed detector outperforms existing ones and overcomes issues due to dataset-dependency.

H.264-based lossless video coding using adaptive transforms

Abstract: In this paper, we propose a reversible video coding method that combines an adaptive transform with H.264 tools. We extensively compare its lossless coding performance against three different reversible transforms and motion JPEG 2000. Experimental results show that for I picture coding, the proposed method performed slightly worse (2.0% lower compression ratio on average) than motion JPEG 2000 while outperforming the new H.264 FRExt standard (9.4 to 14%). For B and P pictures, our method offered the best performance with 0.3 to 6.2% gain over FRExt. Our method requires minimal modification to H.264 software and provides better performance than other existing methods.

Lossless compression of color filter array mosaic images with visualization via JPEG 2000

Abstract: Digital cameras have become ubiquitous for amateur and professional applications. The raw images captured by digital sensors typically take the form of color filter array (CFA) mosaic images, which must be "developed" (via digital signal processing) before they can be viewed. Photographers and scientists often repeat the "development process" using different parameters to obtain images suitable for different purposes. Since the development process is generally not invertible, it is commonly desirable to store the raw (or undeveloped) mosaic images indefinitely. Uncompressed mosaic image file sizes can be more than 30 times larger than those of developed images stored in JPEG format. Thus, data compression is of interest. Several compression methods for mosaic images have been proposed in the literature. However, they all require a custom decompressor followed by development-specific software to generate a displayable image. In this paper, a novel compression pipeline that removes these requirements is proposed. Specifically, mosaic images can be losslessly recovered from the resulting compressed files, and, more significantly, images can be directly viewed (decompressed and developed) using only a JPEG 2000 compliant image viewer. Experiments reveal that the proposed pipeline attains excellent visual quality, while providing compression performance competitive to that of state-of-the-art compression algorithms for mosaic images.

Fast piecewise linear predictors for lossless compression of hyperspectral imagery

Abstract: The work presented here deals with the design of predictors for the lossless compression of hyperspectral imagery. The large number of spectral bands that characterize hyperspectral imagery give it properties that can be exploited when performing compression. Specifically, in addition to the spatial correlation which is similar to all images, the large number of spectral bands means a high spectral correlation also. Lossless compression algorithms are typically divided into two stages, a decorrelation stage and a coding stage. This work deals with the design of predictors for the decorrelation stage which are both fast and good. Fast implies low complexity, which was achieved by having predictors with no multiplications, only comparisons and additions. Good means predictors that have performance close to the state of the art. To achieve this, both spectral and spatial correlations are used for the predictor. The performance of the developed predictors are compared to those in the most widely known algorithms, LOCO-I, used in JPEG-Lossless, and CALIC-Extended, the original version of which had the best compression performance of all the algorithms submitted to the JPEG-LS committee. The developed algorithms are shown to be much less complex than CALIC-Extended with better compression performance.

Detection of operation chain

Abstract: The goal of forensic investigators is to reveal the processing history of a digital image. Many forensic techniques are devoted to detecting the intrinsic traces left by image processing and tampering. However, existing forensic techniques are easily defeated in presence of pre- and post-processing. In real scenarios, images may be sequentially manipulated by a series of operations (the so called operation chain). This paper addresses the operation chain consisting of JPEG compression and resampling. The transformed block artifacts (TBAG) characterizing this operation chain are analysed at both the pixel and discrete cosine transforms (DCT) domain and are utilized to design the detection scheme. Both theoretical analysis and experimental results show the effectiveness of our proposed scheme on identifying the resampled JPEG images as well as the JPEG images undergone resampling and then JPEG recompression. Moreover, the proposed approach can be used to estimate the resampling factors for restoring the whole operation chain. HighlightsOperation chain consists of JPEG compression and Resampling.Detection relies on TBAG and DCTR.Resampling factor can be estimated.