Topic
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.
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15 Oct 2010
TL;DR: The proposed method for detecting resized JPEG images can detect not only conventional resizing but also state-of-the-art non-linear resizing such as seam carving.
Abstract: In this paper, we propose a method for detecting resized JPEG images. We defined 8 × 8 periodic blocks as JPEG blocks. JPEG block boundaries are detected by applying 8 × 8 block discrete cosine transform (DCT) to all the pixels of the input image and analyzing the high frequency coefficients in them. In order to quantitatively analyze the degree of forgery, we have developed two approaches such as truth-score and correlation-score methods. Experimental results using 375 original (untouched) JPEG images and 2,250 resized images recompressed with a variety of quality factors demonstrated that our proposed method can classify them with over 90% of accuracy. Our proposed method can detect not only conventional resizing but also state-of-the-art non-linear resizing such as seam carving.
5 citations
TL;DR: It is concluded that lossless compression of seismic data can savesignificant amounts of storage in seismic data bases and archives, and significant amounts of bandwidth in real- time communication of instrumentation data.
Abstract: Lossless compression is never as profitable, in terms of compression ratio, as lossy compression of the same data. However, lossless techniques that produce significant compression of geophysical waveform data are possible. A two-stage technique for lossless compression of geophysical waveform data is described. The first and most important stage is a form of linear prediction that allows exact recovery of the original waveform data from the predictor residue sequence. The second stage is an encoder of the first-stage residue sequence which approximately maximizes the entropy of the latter, while allowing exact recovery during decompression. We review the overall two-stage technique, which has been described previously, and concentrate in this paper on some recent performance examples and results using the technique. To obtain the latter, a seismic waveform data base is introduced and made available. We conclude that lossless compression of seismic data can save significant amounts of storage in seismic data bases and archives, and significant amounts of bandwidth in real- time communication of instrumentation data.
5 citations
01 Oct 2011
TL;DR: The approach to build integer to integer multiwavelets and experimental results of applying these multi wavelets to lossless image compression are presented.
Abstract: In computer science and information theory, image compression is the process of encoding information using fewer bits than the original representation would use. Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. Image compression may be lossless or lossey. Lossless image compression is a class of image copression algorithms that allows the exact original data to be reconstructed from the compressed data. The term lossless is in contrast to lossy image compression, which only allows an approximation of the original data to be reconstructed, in exchange for better compression rates. Lossless compression is preferred for archival purposes and often for medical imaging, satellite imaging, or technical drawings. For the urgent requirement of efficient lossless compression and high fidelity compression, more and more research of lossless image compression will be concerned. After the introduction of the lifting scheme and the integer to integer multiwavelets, we present the approach to build integer to integer multiwavelets. In addition, experimental results of applying these multiwavelets to lossless image compression are presented.
5 citations
16 Aug 1998
TL;DR: A method is proposed for embedded compression of medical image volumes that uses reversible integer wavelet transforms, and allows both lossy and lossless compression.
Abstract: A method is proposed for embedded compression of medical image volumes. The method uses reversible integer wavelet transforms, and allows both lossy and lossless compression.
4 citations
01 Nov 1993
TL;DR: The Local Cosine Transform is presented as a new method for the reduction and smoothing of the blocking effect that appears at low bit rates in image coding algorithms based on the Discrete Cosine transform.
Abstract: This paper presents the local cosine transform (LCT) as a new method for the reduction and smoothing of the blocking effect that appears at low bit rates in image coding algorithms based on the discrete cosine transform (DCT). In particular, the blocking effect appears in the JPEG baseline sequential algorithm.
4 citations