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 scribblebased 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.
Deblocking filter is one of the most time consuming modules in the H.264/AVC decoder as indicated in many studies. Therefore, accelerating deblocking filter is critical for improving the overall decoding performance. This paper proposes a novel parallel algorithm for H.264/AVC deblocking filter to speed the H.264/AVC decoder up. We exploit pixellevel data parallelism among filtering steps, and observe that results of each filtering step only affect a limited region of pixels. We call this "the limited propagation effect". Based on this observation, the proposed algorithm could partition a frame into multiple independent rectangles with arbitrary granularity. The proposed parallel deblocking filter algorithm requires very little synchronization overhead, and provides good scalability. Experimental results show that applying the proposed parallelization method to a SIMD optimized sequential deblocking filter achieves up to 95.31% and Ja-Ling Wu is a Fellow IEEE. 224.07% speedup on a two-core and four-core processor, respectively. We have also observed a significant speedup for H.264/AVC decoding, 21% and 34% on a two-core and four-core processor, respectively.
The emerging video coding standard, H.264/AVC, exhibits the unprecedented coding performance. Comparing to traditional coders, e.g., MPEG-2 and MEPG-4 ASP, about half bitrate saving is shown in the official verification test. Such outstanding performance makes it become the video compression candidate for the upcoming HD-DVD. As a side effect, it was also blamed that H.264/AVC is much more logically complex and requires more computation power than any of the existing standards. A low-cost and efficient implementation of the international standard hence plays an important role of its success. In this paper, we realize an H.264/AVC baseline decoder by a low-cost DSP processor, i.e., Philips' TriMedia TM-1300, and illustrate that less computation demand for H.264/AVC decoding becomes feasible by using effective software core. To this end, we first consider different approaches and take advantage of SIMD instruction set to optimize critical time-consuming coding modules, such as the fractional motion compensation, spatial prediction and inverse transform. Next, we also present some other optimization approaches for entropy decoding and in-loop deblocking filtering, even though they cannot get benefits from utilizing SIMD. In our experiments, by exploiting appropriate instruction level parallelism and efficient algorithms, the decoding speed can be improved by a factor of 8~10; a CIF video sequence can be decoded at up to 19.74~28.97 fps on a 166-MHz TriMedia TM-1300 processor compared to 2.40~2.98 fps by the standard reference software.
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 wellknown wavefront parallelizing method, respectively.
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.
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