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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01 Jan 1999
TL;DR: This work uses Gradient Adjusted Prediction (GAP) to improve the compression efficiency in text compression and shows that the proposed method is better than lossless JPEG and some LZ-based compression methods.
Abstract: In generally text compression techniques cannot be used directly in image compression because the model of text and image are different. Recently, a new class of text compression, namely, blocksorting algorithm which involves Burrows and Wheeler transformation (BWT) gives excellent results in text compression. However, if we apply it directly into image compression, the result is poor. Surprisingly, good results can be obtained if we employ a prediction model such as the one defined in JPEG standard before the BWT algorithm. Thus, the predictive model plays a critical role in the compression process. To further improve the compression efficiency, we use Gradient Adjusted Prediction (GAP). Experimental results show that the proposed method is better than lossless JPEG and some LZ-based compression methods.
1 citations
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09 Jun 1997TL;DR: In this article, a technique for lossless compression of seismic signals is proposed, which is based on the equation-error structure which approximates the signal by minimizing the error in the least square sense, as a rational function, or equivalently as an auto-regressive moving-average (ARMA) process.
Abstract: A technique for lossless compression of seismic signals is proposed. The algorithm employed is based on the equation-error structure which approximates the signal by minimizing the error in the least square sense, as a rational function, or equivalently as an auto-regressive moving-average (ARMA) process. The algorithm is implemented in the frequency domain. The performance of the proposed technique is compared with the lossless linear predictor for compressing seismic signals. The residual sequence of these schemes is coded using arithmetic coding. The suggested approach yields compression measures, in terms of bits per sample, lower than the lossless linear predictor for compressing different classes of seismic signals.
1 citations
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TL;DR: The experimental results indicate that this algorithm is superior to the standard CCSDS algorithm at both lossless coding efficiency and hardware processing speed, so it meets the need of real time onboard image processing.
Abstract: For the application of onboard remote sensing lossless image compression,two parts of improvement to CCSDS lossless data compression are presented in this paper. The experimental results indicate that this algorithm is superior to the standard CCSDS algorithm at both lossless coding efficiency and hardware processing speed. So it meets the need of real time onboard image processing.
1 citations
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01 Oct 2013TL;DR: The proposed method provides `thumbnail previewing' of the original image from a part of the bit-stream without expanding all the compressed data and avoids degradation of resolution in pixel density of the thumbnail image.
Abstract: This report proposes a new functionality of lossless coding of image signals. The proposed method provides `thumbnail previewing' of the original image from a part of the bit-stream without expanding all the compressed data. It also avoids degradation of resolution in pixel density of the thumbnail image. It is composed of a new lossless color transform and an existing lossless wavelet transform. We add a free parameter to the color transform and utilize it to control a scaling parameter of the luminance component. As a result, it became possible to preview the `thumb-nail' luminance image from a part of the bit-stream. Due to the free parameter, quality and data volume of the `thumbnail' can be controlled according to a users' request. Unlike the existing two layer lossless / lossy coding, the proposed method achieves good performance in lossless coding of the original image signal.
1 citations
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TL;DR: A practical method for estimating the lossless image compression bound based on high-order conditional entropy analysis is proposed, which can be performed within an ordinary image as opposed to dealing with an enormous training set.
Abstract: A practical method for estimating the lossless image compression bound based on high-order conditional entropy analysis is proposed. This analysis can be performed within an ordinary image as opposed to dealing with an enormous training set. Its feasibility and reliability have been verified on a large group of images naturally acquired by various imaging sensors.
1 citations