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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.


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TL;DR: A coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level, which improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline, and is well complementary to many prior techniques.
Abstract: In Bag-of-Words (BoW) based image retrieval, the SIFT visual word has a low discriminative power, so false positive matches occur prevalently. Apart from the information loss during quantization, another cause is that the SIFT feature only describes the local gradient distribution. To address this problem, this paper proposes a coupled Multi-Index (c-MI) framework to perform feature fusion at indexing level. Basically, complementary features are coupled into a multi-dimensional inverted index. Each dimension of c-MI corresponds to one kind of feature, and the retrieval process votes for images similar in both SIFT and other feature spaces. Specifically, we exploit the fusion of local color feature into c-MI. While the precision of visual match is greatly enhanced, we adopt Multiple Assignment to improve recall. The joint cooperation of SIFT and color features significantly reduces the impact of false positive matches. Extensive experiments on several benchmark datasets demonstrate that c-MI improves the retrieval accuracy significantly, while consuming only half of the query time compared to the baseline. Importantly, we show that c-MI is well complementary to many prior techniques. Assembling these methods, we have obtained an mAP of 85.8% and N-S score of 3.85 on Holidays and Ukbench datasets, respectively, which compare favorably with the state-of-the-arts.

206 citations

Patent
M. Vishwanath1, Philip A. Chou1
17 Aug 1995
TL;DR: In this paper, a weighted wavelet hierarchical vector quantization (WWHVQ) procedure is initiated by obtaining an N×N pixel image where 8 bits per pixel are used.
Abstract: A weighted wavelet hierarchical vector quantization (WWHVQ) procedure is initiated by obtaining an N×N pixel image where 8 bits per pixel (steps 10 and 12). A look-up operation is performed to obtain data representing a discrete wavelet transform (DWT) followed by a quantization of the data (step 14). Upon completion of the look-up, a data compression will have been performed. Further stages and look-up will result in further compression of the data, i.e., 4:1, 8:1, 16:1, 32:1, 64:1, . . . etc. Accordingly, a determination is made whether the compression is complete (step 16). If the compression is incomplete, further look-up is performed. If the compression is complete, however, the compressed data is transmitted (step 18). It is determined at a gateway whether further compression is required (step 19). If so, transcoding is performed (step 20). The receiver receives the compressed data (step 22). Subsequently, a second look-up operation is performed to obtain data representing an inverse discrete wavelet transform of the decompressed data (step 24). After one iteration, the data is decompressed by a factor of two. Further iterations allows for further decompression of the data. Accordingly, a determination is made whether decompression is complete (step 26). If the decompression is in incomplete, further look-ups are performed. If, however, the decompression is complete, the WWHVQ procedure is ended (step 28).

205 citations

Proceedings ArticleDOI
25 Apr 2004
TL;DR: Contrary to existing guidelines, it is found that users prefer high-resolution images to high frame rate, and the rule "high motion = highframe rate" does not apply to small screens.
Abstract: We introduce a new methodology to evaluate the perceived quality of video with variable physical quality. The methodology is used to evaluate existing guidelines - that high frame rate is more important than quantization when watching high motion video, such as sports coverage. We test this claim in two studies that examine the relationship between these physical quality metrics and perceived quality. In Study 1, 41 soccer fans viewed CIF-sized images on a desktop computer. Study 2 repeated the experiment with 37 soccer fans, viewing the same content, in QCIF size, on a palmtop device. Contrary to existing guidelines, we found that users prefer high-resolution images to high frame rate. We conclude that the rule "high motion = high frame rate" does not apply to small screens. With small screen devices, reducing quantization removes important information about the players and the ball. These findings have important implications for service providers and designers of streamed video applications.

204 citations

Journal ArticleDOI
TL;DR: Numerical results indicate that images produced by this method offer considerable reduction in the error when compared with images produced from kinoforms made with the random phase method.
Abstract: An analysis of kinoform image reconstruction error is presented. This analysis considers the effects of the error introduced by the kinoform approximation and the quantization effects of plotting. The error measure developed is applied to a proposed method for computing kinoforms. Numerical results indicate that images produced by this method offer considerable reduction in the error when compared with images produced from kinoforms made with the random phase method.

203 citations

Proceedings ArticleDOI
30 Mar 1993
TL;DR: A custom quantization matrix tailored to a particular image is designed by an image-dependent perceptual method incorporating solutions to the problems of luminance and contrast masking, error pooling and quality selectability.
Abstract: A custom quantization matrix tailored to a particular image is designed by an image-dependent perceptual method incorporating solutions to the problems of luminance and contrast masking, error pooling and quality selectability. >

200 citations


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