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.

An evaluation of Error Level Analysis in image forensics

Abstract: The advancement in digital image tampering has encouraged studies in the image forensics fields. The image tampering can be found over various image formats such as Joint Photographic Experts Group (JPEG). JPEG is the most common format that supported by devices and applications. Therefore, researchers have been studying the implementation of JPEG algorithm in the image forensics. In this paper, the Error Level Analysis (ELA) technique was evaluated with different types of image tampering, including JPEG compression, image splicing, copy-move and image retouching. From the experiment, the ELA showed reliability with JPEG compression, image splicing and image retouching forgery.

JPEG compliant efficient progressive image coding

Abstract: Among the different modes of operations allowed in the current JPEG standard, the sequential and progressive modes are the most widely used. While the sequential JPEG mode yields essentially the same level of compression performance for most encoder implementations, the performance of progressive JPEG depends highly upon the designed encoder structure. This is due to the flexibility the standard leaves open in designing progressive JPEG encoders. In this paper, a rate-distortion optimized JPEG compliant progressive encoder is presented that produces a sequence of bit scans, ordered in terms of decreasing importance. Our encoder outperforms a baseline sequential JPEG encoder in terms of compression, significantly at medium bit rates, and substantially at low and high bit rates. Moreover, unlike baseline JPEG encoders, ours can achieve precise rate/distortion control. Good rate-distortion performance at low bit rates and precise rate control, provided by our JPEG compliant progressive encoder, are two highly desired features currently sought for JPEG-2000.

Near optimal detection of quantized signals and application to JPEG forensics

Abstract: In this paper, we investigate the problem of deciding whether a multidimensional signal has been quantized according to a given lattice or not Under an infinite variance assumption, we derive the expression of the optimal detector, together with a practical approximation formula based on multidimensional Fourier series As a forensic case study, the proposed detector is applied to the detection of nonaligned double JPEG compression Results on both synthetic signals and real JPEG images show interesting properties of the proposed detector Namely, the detector outperforms existing state-of-art detectors for nonaligned double JPEG compression The application of the proposed scheme to other forensic problems seems a natural extension of this work

JPEG2000 compatible lossless coding of floating-point data

Abstract: Many scientific applications require that image data be stored in floating-point format due to the large dynamic range of the data. These applications pose a problem if the data needs to be compressed since modern image compression standards, such as JPEG2000, are only defined to operate on fixed-point or integer data. This paper proposes straightforward extensions to the JPEG2000 image compression standard which allow for the efficient coding of floating-point data. These extensions maintain desirable properties of JPEG2000, such as lossless and rate distortion optimal lossy decompression from the same coded bit stream, scalable embedded bit streams, error resilience, and implementation on low-memory hardware. Although the proposed methods can be used for both lossy and lossless compression, the discussion in this paper focuses on, and the test results are limited to, the lossless case. Test results on real image data show that the proposed lossless methods have raw compression performance that is competitive with, and sometime exceeds, current state-of-the-art methods.

Matched-texture coding for structurally lossless compression

Abstract: We propose a new texture-based compression approach that relies on new texture similarity metrics and is able to exploit texture redundancies for significant compression gains without loss of visual quality, even though there may visible differences with the original image (structurally lossless). Existing techniques rely on point-by-point metrics that cannot account for the stochastic and repetitive nature of textures. The main idea is to encode selected blocks of textures - as well as smooth blocks and blocks containing boundaries between smooth and/or textured regions - by pointing to previously occurring (already encoded) blocks of similar textures, blocks that are not encoded in this way, are encoded by a baseline method, such as JPEG. Experimental results with natural images demonstrate the advantages of the proposed approach.