This paper addresses the optimization problem of minimizing the number of memory access subject to a rate constraint for any Huffman decoding of various standard codecs. We propose a Lagrangian multiplier based penalty-resource metric to be the targeting cost function. To the best of our knowledge, there is few related discussion, in the literature, on providing a criterion to judge the approaches of entropy decoding under resource constraint. The existing approaches which dealt with the decoding of the single-side growing Huffman tree may not be memory-efficient for arbitrary-side growing Huffman trees adopted in current codecs. By grouping the common prefix part of a Huffman tree, in stead of the commonly used single-side growing Huffman tree, we provide a memory efficient hierarchical lookup table to speed up the Huffman decoding. Simulation results show that the proposed hierarchical table outperforms previous methods. A Viterbi-like algorithm is also proposed to efficiently find the optimal hierarchical table. More importantly, the Viterbi-like algorithm obtains the same results as that of the brute-force search algorithm.

In this paper, we propose a parallel algorithm for H.264/AVC deblocking filter which is scalable to the number of processors. Unlike the conventional approach, which is limited by the independent data units, the designed algorithm allows issuing dependent data units concurrently to decrease the penalty from synchronization of data units. For the general-purpose dual-core processors, experimental results show that our method speeds up 1.72 and 1.39 times as compared with optimized sequential method and the well-known wavefront parallelizing method, respectively.

By grouping the common prefix of a Huffman tree, in stead of the commonly used single-side rowing Huffman tree (SGH-tree), we construct a memory efficient Huffman table on the basis of an arbitrary-side growing Huffman tree (AGH-tree) to speed up the Huffman decoding. Simulation results show that, in Huffman decoding, an AGH-tree based Huffman table is 2.35 times faster that of the Hashemian's method (an SGH-tree based one) and needs only one-fifth the corresponding memory size. In summary, a novel Huffman table construction scheme is proposed in this paper which provides better performance than existing construction schemes in both decoding speed and memory usage

Colorization is a computer-assisted process for adding colors to grayscale images or movies. It can be viewed as a process for assigning a three-dimensional color vector (YUV or RGB) to each pixel of a grayscale image. In previous works, with some color hints the resultant chrominance value varies linearly with that of the luminance. However, it is easy to find that existing methods may introduce obvious color bleeding, especially, around region boundaries. It then needs extra human-assistance to fix these artifacts, which limits its practicability. Facing such a challenging issue, we introduce a general and fast colorization methodology with the aid of an adaptive edge detection scheme. By extracting reliable edge information, the proposed approach may prevent the colorization process from bleeding over object boundaries. Next, integration of the proposed fast colorization scheme to a scribble-based colorization system, a modified color transferring system and a novel chrominance coding approach are investigated. In our experiments, each system exhibits obvious improvement as compared to those corresponding previous works.