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Quantization (image processing)

About: Quantization (image processing) is a research topic. Over the lifetime, 7977 publications have been published within this topic receiving 126632 citations.


Papers
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Proceedings ArticleDOI
30 May 1999
TL;DR: It is demonstrated through some simple experiments that for a given image reconstruction quality, more scalar parameters must be transmitted using the SVD, than when using the discrete cosine transform (DCT).
Abstract: During the past couple of decades several proposals for image coders using singular value decomposition (SVD) have been put forward. The results using SVD in this context have never been spectacular. The main problem with the SVD is that the transform itself must be transmitted as side information. We demonstrate through some simple experiments that for a given image reconstruction quality, more scalar parameters must be transmitted using the SVD, than when using the discrete cosine transform (DCT). Also, using an alternative interpretation of the SVD we show that the SVD representation necessitates quantization of individual factors as compared to quantization of the associated product. This is clearly suboptimal.

59 citations

Proceedings ArticleDOI
01 Dec 2012
TL;DR: The experimental results show that the proposed algorithm apparently preserves superior image quality and robustness under various attacks such as JPEG compression, cropping, sharping, contrast adjustments and so on.
Abstract: A combined DWT and DCT based watermarking technique with low frequency watermarking with weighted correction is proposed DWT has excellent spatial localization, frequency spread and multi-resolution characteristics, which are similar to the theoretical models of the human visual system (HVS) DCT based watermarking techniques offer compression while DWT based watermarking techniques offer scalability These desirable properties are used in this combined watermarking technique In the proposed method watermark bits are embedded in the low frequency band of each DCT block of selected DWT sub-band The weighted correction is also used to improve the imperceptibility The extracting procedure reverses the embedding operations without the reference of the original image Compared with the similar approach by DCT based approach and DWT based approach, the experimental results show that the proposed algorithm apparently preserves superior image quality and robustness under various attacks such as JPEG compression, cropping, sharping, contrast adjustments and so on

59 citations

Journal ArticleDOI
TL;DR: Experimental results prove that the proposed image fusion method generates better effects on both visual perception and objective quantization than traditional methods.

59 citations

Patent
09 Apr 2007
TL;DR: In this article, a video encoder identifies portions of a video picture that contain DC shift blocks and adjusts quantization (e.g., by selecting a smaller quantization step size) to reduce contouring artifacts when the picture is reconstructed.
Abstract: A video encoder identifies one or more portions of a video picture that contain DC shift blocks and adjusts quantization (e.g., by selecting a smaller quantization step size) to reduce contouring artifacts when the picture is reconstructed. The encoder can identify the portion(s) of the picture that contain DC shift blocks by identifying one or more gradient slope regions in the picture and analyzing quantization effects on DC coefficients in the gradient slope region(s). The encoder can select a coarser quantization step size for a high-texture picture portion.

58 citations

Book ChapterDOI
25 Apr 2010
TL;DR: A set of domain specific loss-less compression schemes that achieve over 40× compression of fragments, outperforming bzip2 by over 6× are introduced and the study of using ‘lossy' quality values is initiated.
Abstract: With the advent of next generation sequencing technologies, the cost of sequencing whole genomes is poised to go below $1000 per human individual in a few years As more and more genomes are sequenced, analysis methods are undergoing rapid development, making it tempting to store sequencing data for long periods of time so that the data can be re-analyzed with the latest techniques The challenging open research problems, huge influx of data, and rapidly improving analysis techniques have created the need to store and transfer very large volumes of data. Compression can be achieved at many levels, including trace level (compressing image data), sequence level (compressing a genomic sequence), and fragment-level (compressing a set of short, redundant fragment reads, along with quality-values on the base-calls) We focus on fragment-level compression, which is the pressing need today. Our paper makes two contributions, implemented in a tool, SlimGene First, we introduce a set of domain specific loss-less compression schemes that achieve over 40× compression of fragments, outperforming bzip2 by over 6× Including quality values, we show a 5× compression using less running time than bzip2 Second, given the discrepancy between the compression factor obtained with and without quality values, we initiate the study of using ‘lossy' quality values Specifically, we show that a lossy quality value quantization results in 14× compression but has minimal impact on downstream applications like SNP calling that use the quality values Discrepancies between SNP calls made between the lossy and lossless versions of the data are limited to low coverage areas where even the SNP calls made by the lossless version are marginal.

58 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20228
2021354
2020283
2019294
2018259
2017295