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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.


Papers
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Proceedings ArticleDOI
24 Oct 1999
TL;DR: A unified coding algorithm for lossless and near-lossless color image compression that exploits the correlations between RGB signals that can control the distortion level in the magnitude on the RGB plane is proposed.
Abstract: This paper proposes a unified coding algorithm for lossless and near-lossless color image compression that exploits the correlations between RGB signals. For lossless coding, a reversible color transform is proposed that removes the correlations between RGB signals while avoiding any finite word length limitation. Next, the lossless algorithm is extended to a unified coding algorithm of lossless and near-lossless compression that can control the distortion level in the magnitude on the RGB plane. Experimental results show the effectiveness of the proposed algorithm.

12 citations

Proceedings ArticleDOI
E. Yeung1
25 May 1997
TL;DR: In this article, the authors introduced the theory behind the wavelet transform and overviewed the implementation of a wavelet image compressor, and concluded that wavelet compression is a better choice over JPEG compression, especially when very high compression ratios are required.
Abstract: This paper briefly introduces the theory behind the wavelet transform and overviews the implementation of a wavelet image compressor. Emphasis is placed on the implementation and efficiency of a system, rather than the intriguing mathematical details. Both quantitative and subjective evaluations were performed on images compressed with the wavelet compressor, and comparisons were made with images compressed with JPEG compression. It is concluded that wavelet compression is a better choice over JPEG compression, especially when very high compression ratios are required.

12 citations

Proceedings ArticleDOI
13 May 2002
TL;DR: The proposed scheme gives, on average, smaller lossless compression bit rate, however, this improvement in performance is achieved at the expense of an increase in computational complexity.
Abstract: In this paper we introduce the use of adaptive filter banks in lossless compression of images with progressive coding in resolution. During the decomposition the filter adapts itself automatically to various regions of the image, preserving the perfect reconstruction property. Effects of parameters used in the decomposition have been studied. Simulation results are given and compared with well-known codecs. The proposed scheme gives, on average, smaller lossless compression bit rate. However, This improvement in performance is achieved at the expense of an increase in computational complexity.

12 citations

Posted Content
TL;DR: In this paper, the authors proposed a method to detect double or single JPEG compression using convolutional neural networks (CNNs) and different kinds of input to the CNN have been taken into consideration.
Abstract: When an attacker wants to falsify an image, in most of cases she/he will perform a JPEG recompression. Different techniques have been developed based on diverse theoretical assumptions but very effective solutions have not been developed yet. Recently, machine learning based approaches have been started to appear in the field of image forensics to solve diverse tasks such as acquisition source identification and forgery detection. In this last case, the aim ahead would be to get a trained neural network able, given a to-be-checked image, to reliably localize the forged areas. With this in mind, our paper proposes a step forward in this direction by analyzing how a single or double JPEG compression can be revealed and localized using convolutional neural networks (CNNs). Different kinds of input to the CNN have been taken into consideration, and various experiments have been carried out trying also to evidence potential issues to be further investigated.

12 citations

Proceedings ArticleDOI
27 Jun 2007
TL;DR: A perceptual image coder for the compression of monochrome images is presented here, in which the coding structure is coupled with a vision model to produce coded images with an improved visual quality at low bit-rates.
Abstract: A perceptual image coder for the compression of monochrome images is presented here, in which the coding structure is coupled with a vision model to produce coded images with an improved visual quality at low bit-rates. The coder is an improvement on the Joint Photographic Experts Group (JPEG -Discrete Cosine Transform (DCT) based) image compression standard, and the structure can be easily extended to implement as an improvement on the new JPEG standard. The proposed coding structure incorporates the human vision model in all stages of compression, and gives very good results compared to the existing JPEG standard. Though the mathematical model used are not new, the simple structure of it which is similar to the coding structure of the JPEG, incorporates the visual processing stages in a very systematic manner in the same way as the visual neurons process the signal. The results presented in this paper reveal that the proposed structure gives very good perceptual quality compared to the JPEG scheme especially as we go for lower bit rates. One of the major advantages of the proposed scheme is that it can be easily extended to a structure in which the rate control optimization can be incorporated, as in the new JPEG scheme.

12 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202321
202240
20215
20202
20198
201815