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.

Frame rate up-conversion is a postprocessing tool to convert the frame rate from a lower number to a higher one. It is a useful technique for a lot of practical applications, such as display format conversion, low bit rate video coding and slow motion playback. Unlike traditional approaches, such as frame repetition or linear frame interpolation, motion-compensated frame interpolation (MCFI) technique is more efficient since it takes block motion into account. In this paper, by considering the deficiencies of previous works, new criteria and coding schemes for improving motion derivation and interpolation processes are proposed. Next, for video coding applications, adaptive frame skip is executed at the encoder side to maximize the power of MCFI so that the quality of interpolated frames is guaranteed. Experimental results show that our proposal effectively enhances the overall quality of the frame rate up-converted video sequence, both subjectively and objectively.

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.

Fine granularity scalability (FGS) is the latest video-coding tool provided in Amendment 2 of the MPEG-4 standard. By taking advantage of bitplane coding of DCT residues, the compressed bitstream can tie truncated at any location to support the finest rate scalability in the enhancement layer. However, both frame buffer scanning several times in bitplane decoding and frame duplication simultaneously for base and enhancement layer decoding make FGS difficult in its implementation. We propose a corresponding pair of efficient streaming schedule and pipeline decoding architecture to deal with the prescribed problems. The design may be applied to the case of streaming stored FGS videos and benefit FGS-related applications.