Zhibin Pan

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Vector quantization (VQ) is an asymmetric coding method and the winner search in encoding process is extremely time-consuming. This property of VQ constrains its practical applications very much. Based on the sum pyramid data structure of a vector, a fast encoding algorithm with PSNR equivalent to full search is proposed in this paper to improve the results(More)
The encoding process of vector quantization (VQ) is very heavy and it constrains VQ's application to a great deal. In order to speed up VQ encoding, it is most important to avoid unnecessary Euclidean distance computation (k-D) as much as possible by the difference check first that uses simpler features (low dimensional) while winner searching is going on.(More)
This paper proposes a new greedy algorithm combining the semi-supervised learning and the sparse representation with the data-dependent hypothesis spaces. The proposed greedy algorithm is able to use a small portion of the labeled and unlabeled data to represent the target function, and to efficiently reduce the computational burden of the semi-supervised(More)
In the framework of vector quantization (VQ), fast search method is a key issue because it is the time bottleneck in VQ encoding process. To speed up VQ, some very effective fast search methods that are based on using statistical features (i.e. the mean, the variance and L 2 norm) of a k-dimensional vector have already been proposed in previous works [2],(More)