The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent comp...
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The evolution of social network and multimedia technologies encourage more and more people to generate and upload visual information, which leads to the generation of large-scale video data. Therefore, preeminent compression technologies are highly desired to facilitate the storage and transmission of these tremendous video data for a wide variety of applications. In this paper, a systematic review of the recent advances for large-scale video compression （LSVC） is presented. Specifically, fast video coding algorithms and effective models to improve video compression efficiency are introduced in detail, since coding complexity and compression efficiency are two important factors to evaluate videocoding approaches. Finally, the challenges and fu- ture research trends for LSVC are discussed.
Machine learning approaches have been increasingly used to reduce the high computational complexity of high-efficiency videocoding (HEVC), as this is a major limiting factor for real-time implementations, due to the ...
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Machine learning approaches have been increasingly used to reduce the high computational complexity of high-efficiency videocoding (HEVC), as this is a major limiting factor for real-time implementations, due to the decision process required to find optimal coding modes and partition sizes for the quad-tree data structures defined by the standard. This paper proposes a systematic approach to reduce the computational complexity of HEVC based on an ensemble of online and offline Random Forests classifiers. A reduced set of features for training the Random Forests classifier is proposed, based on the rankings obtained from information gain and a wrapper-based approach. The best model parameters are also obtained through a consistent and generalizable method. The proposed Random Forests classifier is used to model the coding unit and transform unit-splitting decision and the SKIP-mode prediction, as binary classification problems, taking advantage from the combination of online and offline approaches, which adapts better to the dynamic characteristics of video content. Experimental results show that, on average, the proposed approach reduces the computational complexity of HEVC by 62.64% for the random access (RA) profile and 54.57% for the low-delay (LD) main profile, with an increase in BD-Rate of 2.58% for RA and 2.97% for LD, respectively. These results outperform the previous works also using ensemble classifiers for the same purpose.
The H.264 standard provides significantly high compression efficiency with multiple block size Motion Estimation adopted. However, the encoding time of Motion Estimation and Discrete Cosine Transform is dramatically i...
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ISBN:
(纸本)9781424445936
The H.264 standard provides significantly high compression efficiency with multiple block size Motion Estimation adopted. However, the encoding time of Motion Estimation and Discrete Cosine Transform is dramatically increased as a result. An early mode decision algorithm is proposed in this paper to control the computation complexity. It derives a threshold to make mode decisions in advance. Experimental results show that 75.11% encoding time and 84.25% Motion estimation time can be saved on average with little PSNR dropped.
The High Efficiency videocoding (HEVC) standard provides a substantial improvement in coding efficiency over previous videocoding standards at the cost of a higher computational complexity. HEVC employs a quadtree b...
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ISBN:
(纸本)9781479999880
The High Efficiency videocoding (HEVC) standard provides a substantial improvement in coding efficiency over previous videocoding standards at the cost of a higher computational complexity. HEVC employs a quadtree based image structure by partitioning the image into coding units (CUs). Finding the optimal CU size in terms of rate-distortion is one of the most computationally challenging parts of any HEVC encoder. Previous works for fast CU size selection are usually based on data dependency between neighboring CUs and therefore limit the degree of possible parallelism. In this paper, we present a fast CU size selection method that does not depend on any data from other CUs in the same frame, thus allowing utilization of the high parallel processing capability of many-core processors, such as a GPU. Experimental results show that the proposed method incurs only a negligible loss in rate-distortion performance compared with counterpart methods that limit parallelism.
The High Efficiency videocoding (HEVC) standard provides a substantial improvement in coding efficiency over previous videocoding standards at the cost of a higher computational complexity. HEVC employs a quadtree b...
详细信息
ISBN:
(纸本)9781479999897
The High Efficiency videocoding (HEVC) standard provides a substantial improvement in coding efficiency over previous videocoding standards at the cost of a higher computational complexity. HEVC employs a quadtree based image structure by partitioning the image into coding units (CUs). Finding the optimal CU size in terms of rate-distortion is one of the most computationally challenging parts of any HEVC encoder. Previous works for fast CU size selection are usually based on data dependency between neighboring CUs and therefore limit the degree of possible parallelism. In this paper, we present a fast CU size selection method that does not depend on any data from other CUs in the same frame, thus allowing utilization of the high parallel processing capability of many-core processors, such as a GPU. Experimental results show that the proposed method incurs only a negligible loss in rate-distortion performance compared with counterpart methods that limit parallelism.
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