The rate-distortion optimized quantization (RDOQ) provides an excellent trade-off between rate and distortion in High Efficiency videocoding (HEVC), leading to inspiring improvement in terms of rate-distortion perfor...
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The rate-distortion optimized quantization (RDOQ) provides an excellent trade-off between rate and distortion in High Efficiency videocoding (HEVC), leading to inspiring improvement in terms of rate-distortion performance. However, its heavy use imposes high complexity on the encoder in real-world video compression applications. In this paper, we provide a comprehensive review on lowcomplexity quantization techniques in HEVC, including both fast RDOQ and all-zero block detection. In particular, the fast RDOQ relies on rate and distortion models for rate-distortion cost estimation, such that the most appropriate quantized coefficient is selected in a lowcomplexity way. All-zero block detection infers the all-zero block by skipping transform and quantization, in an effort to further reduce the complexity. The relationship between the two techniques is also discussed, and moreover, we also envision the future design of lowcomplexity quantization in the upcoming Versatile videocoding (VVC) standard.
With the popularity of high-efficiency videocoding (HEVC) standard, a video server usually transcodes a video stream to HEVC for its higher compression ratio. In this paper, a fast H.264/advanced videocoding (AVC) t...
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With the popularity of high-efficiency videocoding (HEVC) standard, a video server usually transcodes a video stream to HEVC for its higher compression ratio. In this paper, a fast H.264/advanced videocoding (AVC) to HEVC transcoding method is proposed. In the HEVC encoding procedure, a coding unit (CU), which is a motion-homogeneous block, is first checked based on the analysis of the decoded information from H.264/AVC bit stream. Then, for motion-homogeneous blocks, CU depth and the corresponding prediction unit (PU) mode's early termination strategies are proposed based on the CU size and corresponding prior statistical knowledge. For non-motion-homogeneous blocks, a corresponding PU mode's early termination strategy is also proposed. Experimental results demonstrate the effectiveness of the proposed method.
High Efficiency videocoding (HEVC) encoder provides higher compression efficiency by offering 35 intra modes. However, the encodingcomplexity is increased due to more modes are involved in the decision process. Ther...
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High Efficiency videocoding (HEVC) encoder provides higher compression efficiency by offering 35 intra modes. However, the encodingcomplexity is increased due to more modes are involved in the decision process. Therefore, it is desired to build a fast intra prediction algorithm that is practical for real time application. In this paper, a quadratic approach for reducing intra codingcomplexity is proposed. Firstly, the relationship between the RD-cost and the SAD is investigated. Secondly, a model is proposed to estimate the RD-cost of all 35 intra modes using the quadratic relation, thus avoiding the computation of entropy coding, Hadamard cost, distortion, and transform. Experimental results demonstrate that the average time saving of the proposed approach is 31-38%, while the BD-Bit Rate increment is only 0.62-1.37%, respectively. (C) 2015 Elsevier Inc. All rights reserved.
High efficiency videocoding (HEVC) uses a quadtree-based structure for coding unit (CU) splitting to effectively encode various video sequences with different visual characteristics. However, this new structure resul...
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ISBN:
(纸本)9781728180588
High efficiency videocoding (HEVC) uses a quadtree-based structure for coding unit (CU) splitting to effectively encode various video sequences with different visual characteristics. However, this new structure results in a dramatically increased complexity that makes real-time HEVC encoding very challenging. In this paper, we propose a novel CU size decision method based on deep reinforcement learning and active feature acquisition to reduce HEVC intra coding computational complexity and encoding time. The proposed method carries out early splitting and early splitting termination by considering the encoder and CU as an agent-environment system. More specifically, through early splitting, the proposed method precludes the need for rate-distortion optimization at the current level. In addition, through early splitting termination, it disposes of the lower level computations. The proposed method provides a very fast encoder with a small quality penalty. Experimental results show that it achieves a 51.3% encoding time reduction on average with a small quality loss of 0.041 dB for the BD-PSNR, when we compare our method to the HEVC test model.
Distributed videocoding (DVC) is a new coding paradigm for video compression mainly based on two major information theory results, Slepian and Wolf's and Wyner-Ziv theorems. In some major ap- plications such as w...
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ISBN:
(纸本)9781424445189
Distributed videocoding (DVC) is a new coding paradigm for video compression mainly based on two major information theory results, Slepian and Wolf's and Wyner-Ziv theorems. In some major ap- plications such as wireless low power surveillance and multimedia sensor networks, wireless PC cameras and mobile camera phones, it is really a challenge for traditional videocoding architecture. For both encoder and decoder, it is necessary to have a low power consumption for some cases. So low power and lowcomplexity encoder are essential to satisfy those requirements. This paper reviews some recent developments of dis- tributed videocoding. Slepian and Wolf's coding is the lossless source coding and Wyner-Ziv coding is a lossy compression with receiver side information. It has been observed that in most of the traditional videocoding algorithm such as MPEG,H.263+ or H.264 the encoder has com- plex computational operation rather than the decoder. The distributed videocoding is a new kind of videocoding which allows a lowcomplexityvideo encoding where the major part of computational burden is shifted to decoder. DVC is mainly applicable to two areas that is low complex- ity videocoding and robust videocoding. The dierent techniques are analysed based on certain parameters such as compression rate, decoding and motion compensation.
Wireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system using a battery-powered electronic capsule. It is considered a des...
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Wireless capsule endoscopy (WCE) can be viewed as an innovative technology introduced in the medical domain to directly visualize the digestive system using a battery-powered electronic capsule. It is considered a desirable substitute for conventional digestive tract diagnostic methods for a comfortable and painless inspection. Despite many benefits, WCE results in poor video quality due to low frame resolution and diagnostic accuracy. Many research groups have presented diversified, low-complexity compression techniques to economize battery power consumed in the radio-frequency transmission of the captured video, which allows for capturing the images at high resolution. Many vision-based computational methods have been developed to improve the diagnostic yield. These methods include approaches for automatically detecting abnormalities and reducing the amount of time needed for video analysis. Though various research works have been put forth in the WCE imaging field, there is still a wide gap between the existing techniques and the current needs. Hence, this article systematically reviews recent WCE video compression and summarization techniques. The review's objectives are as follows: First, to provide the details of the requirement, challenges and design percepts for the lowcomplexity WCE video compressor. Second, to discuss the most recent compression methods, emphasizing simple distributed videocoding methods. Next, to review the most recent summarization techniques and the significance of using deep neural networks. Further, this review aims to provide a quantitative analysis of the state-of-the-art methods along with their advantages and drawbacks. At last, to discuss existing problems and possible future directions for building a robust WCE imaging framework.
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