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.

Discrete cosine transform JPEG compression vs. 2D JPEG2000 compression: JNDmetrix visual discrimination model image quality analysis

Abstract: The JPEG2000 compression standard is increasingly a preferred industry method for 2D image compression. Some vendors, however, continue to use proprietary discrete cosine transform (DCT) JPEG encoding. This study compares image quality in terms of just-noticeable differences (JNDs) and peak signal-to-noise ratios (PSNR) between DCT JPEG encoding and JPEG2000 encoding. Four computed tomography and 6 computed radiography studies were compressed using a proprietary DCT JPEG encoder and JPEG2000 standard compression. Image quality was measured in JNDs and PSNRs. The JNDmetrix computational visual discrimination model simulates known physiological mechanisms in the human visual system, including the luminance and contrast sensitivity of the eye and spatial frequency and orientation responses of the visual cortex. Higher JND values indicate that a human observer would be more likely to notice a significant difference between compared images. DCT JPEG compression showed consistently lower image distortions at lower compression ratios, whereas JPEG2000 compression showed benefit at higher compression ratios (>50:1). The crossover occurred at ratios that varied among the images. The magnitude of any advantage of DCT compression at low ratios was often small. Interestingly, this advantage of DCT JPEG compression at lower ratios was generally not observed when image quality was measured in PSNRs. These results suggest that DCT JPEG may outperform JPEG2000 for compression ratios generally used in medical imaging and that the differences between DCT and JPEG2000 could be visible to observers and thus clinically significant.

Lossless image compression using gradient based space filling curves (G-SFC)

Abstract: In most classical lossless image compression schemes, images are scanned line by line, and so, only horizontal patterns are effectively compressed. The proposed approach attempts to better explore image correlation in different directions by adopting a context-based adaptive scanning process. The adopted scanning process aims to generate a compact one-dimensional image representation by using an image gradient based scan process. This process tries to find the best space-filling curve that ensures scanning the image according to the direction where minimal pixels’ intensity change is found. Such scan process would reduce high frequency data. It is used in order to provide an easily compressible smooth and highly correlated mono-dimensional signal. The suggested representation acts as a pre-processing which transforms the image source into some strongly correlated representation before applying coding algorithms. Based on this representation, a new lossless image compression method is designed. Our experimental results show that the proposed image representation is able to significantly improve the signal proprieties in terms of correlation and monotony and then compression performances. The suggested coding scheme shows a competitive compression results compared to conventional lossless coding schemes such as PNG and JPEG 2000.

Image encryption schemes for JPEG and GIF formats based on 3D baker with compound chaotic sequence generator

Abstract: This paper proposed several methods to transplant the compound chaotic image encryption scheme with permutation based on 3D baker into image formats as Joint Photographic Experts Group (JPEG) and Graphics Interchange Format (GIF). The new method averts the lossy Discrete Cosine Transform and quantization and can encrypt and decrypt JPEG images lossless. Our proposed method for GIF keeps the property of animation successfully. The security test results indicate the proposed methods have high security. Since JPEG and GIF image formats are popular contemporarily, this paper shows that the prospect of chaotic image encryption is promising.

JPEG quality transcoding using neural networks trained with a perceptual error measure

Abstract: A JPEG Quality Transcoder (JQT) converts a JPEG image file that was encoded with low image quality to a larger JPEG image file with reduced visual artifacts, without access to the original uncompressed image. In this article, we describe technology for JQT design that takes a pattern recognition approach to the problem, using a database of images to train statistical models of the artifacts introduced through JPEG compression. In the training procedure for these models, we use a model of human visual perception as an error measure. Our current prototype system removes 32.2% of the artifacts introduced by moderate compression, as measured on an independent test database of linearly coded images using a perceptual error metric. This improvement results in an average PSNR reduction of 0.634 dB.