Advances in Multimedia Information Processing – PCM 2012: by Chao Wang, Yunhong Wang, Zhaoxiang Zhang (auth.), Weisi Lin,

By Chao Wang, Yunhong Wang, Zhaoxiang Zhang (auth.), Weisi Lin, Dong Xu, Anthony Ho, Jianxin Wu, Ying He, Jianfei Cai, Mohan Kankanhalli, Ming-Ting Sun (eds.)

This e-book constitutes the court cases of the thirteenth Pacific Rim convention on Multimedia, held in Singapore in the course of December 4-6, 2012. The fifty nine revised complete papers awarded have been rigorously reviewed and chosen from 106 submissions for the most convention and are observed by way of 23 displays of four exact classes. The papers are prepared in topical sections on multimedia content material research, picture and video processing, video coding and multimedia details processing, image/video processing and research, video coding and multimedia process, complicated photo and video coding, pass media studying with structural priors, in addition to effective multimedia research and utilization.

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Extra info for Advances in Multimedia Information Processing – PCM 2012: 13th Pacific-Rim Conference on Multimedia, Singapore, December 4-6, 2012. Proceedings

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Comparison of sensitivity to quality changes under different conditions 5 Conclusions In this work the experimental setup and results of a long duration audiovisual content audio quality experiment are presented. It has been found that participants’ preferences regarding audio quality are relatively constant with respect to time. A mixed-effects ANOVA model was used to gain insight into the relationship between means of specific factors. It turned out that the time factor does not influence quality ratings, whereas the between-user factor does.

Specifically, hamming ranking means that, according to Hamming distance, all the data points are ranked and the desired neighbors are returned from the top of ranked list. It apparently relates to hash quality and its time complexity is linear. Second, Hash lookup indicates that a lookup table is built by utilizing the hashing codewords, and it stresses search speed. Hash lookup often fails owing to sparse Hamming space, and as a consequence the strategy is that neighbors with Hamming radius r of the query are returned and Hash lookup time complexity is constant time.

And W ˜ = b . . ⎦ W · · · wsK Clearly, the first term in (3) is a loss function that measures the similarity between the outputs of hashing functions and the hashing codes obtained in Sect. 1, and the second term W 2 is to avoid overfitting. λ is a trade-off parameters that can balance the loss function and the regularization term. Therefore, the optimal solution of above regularized kernel least squares (3) can be obtained ˜ = (λI + KT K)−1 KT HT . 3 27 Analysis Combined with the above two stages, the whole flowchart of Hashing algorithm with Cauchy Graph Embedding is summarized in the following Algo.

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