With the advance of the 5th generation (5G) wireless system technology, the JVET started to develop a FVC (Future Video coding) standard for the ultra-high definition video (UHDV) since 2016. We study how to speed up ...
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ISBN:
(纸本)9781728105253
With the advance of the 5th generation (5G) wireless system technology, the JVET started to develop a FVC (Future Video coding) standard for the ultra-high definition video (UHDV) since 2016. We study how to speed up the H.266 coding without degrading the coding quality. The FVC/H.266 adopts Quadtree plus Binary tree (QTBT) structure for codingunits (CU). We proposed to reference the average depth information of neighboring Largest codingunit (LCU) to determine whether to early terminate CU decomposition or not. By utilizing the coding modes of neighboring CUs, it can effectively eliminate unnecessary rate-distortion optimization (RDO) operations. Correlations of coding modes between neighboring LCUs are not strong and the mode prediction rule of a current CU is developed based on heuristic approaches. Experiments showed that the proposed method can save up 25.42% of processing time, while the BDBR rises only 0.31%, as compared to the JEM system program. How to reduce the time complexity of HEVC/H.265 and FVC/H.266 can be formulated as a problem solving through neural network models, which is expected to yield much more time complexity reduction under the same video coding quality.
The rapid development of the internet has led to a surge in demand for video traffic, presenting new challenges for the storage and transmission of video data. In this paper, considering the semantic information of vi...
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ISBN:
(纸本)9798350372267;9798350372250
The rapid development of the internet has led to a surge in demand for video traffic, presenting new challenges for the storage and transmission of video data. In this paper, considering the semantic information of video frames, semantic aware-based algorithms for coding tree unit (CTU) and group of picture (GOP) have been designed for video encoding. The proposed algorithm utilizes deep learning to forecast the outcomes of CTU splitting in video coding via trained models. Furthermore, a semantic aware GOP spliting approach is developed. Experimental results confirm that the proposed algorithms can improve the performance of video compression. The proposed CTU spliting algorithm saves a significant amount of intra-frame encoding time, and the dynamic GOP spliting algorithm achieves quality improvement at the same bit rate.
A novel SSIM based RDO approach for intra-only coding in HEVC is presented. We incorporate SSIM into the distortion measure for variable size CTUs. The corresponding Lagrange multiplier is also derived. The experiment...
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ISBN:
(纸本)9781479912919
A novel SSIM based RDO approach for intra-only coding in HEVC is presented. We incorporate SSIM into the distortion measure for variable size CTUs. The corresponding Lagrange multiplier is also derived. The experimental results show that the proposed RDO approach achieves 8.54% bit-rate reduction on average.
In High Efficiency Video coding (HEVC), the coding efficiency of I-frames is lower than P-frames and B-frames, which will cause the flicker artifact, especially in low bitrates applications. We propose a region-classi...
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ISBN:
(纸本)9781479923410
In High Efficiency Video coding (HEVC), the coding efficiency of I-frames is lower than P-frames and B-frames, which will cause the flicker artifact, especially in low bitrates applications. We propose a region-classification-based rate control for coding tree units (CTUs) in I-frames to improve the reconstructed quality of I-frames to suppress the flicker artifact. The CTUs in I-frame are classified into three regions according to their motion vectors and complexity. When the bit budget of one I-frame is used up, the target bitrates for the remaining CTUs will be adjusted according to the regions they belong to, and the pixel-based unified rate-quantization (URQ) model is then used to calculate the QPs. Experimental results demonstrate that the proposed scheme can efficiently suppress the flicker artifacts and improve both the subjective and objective video quality when compared with the original scheme in HM9.0.
In this paper, we present a computer cluster with heterogeneous computing components intended to provide concurrency and parallelism with embedded processors to achieve a real-time Multi-View High-Efficiency Video Cod...
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In this paper, we present a computer cluster with heterogeneous computing components intended to provide concurrency and parallelism with embedded processors to achieve a real-time Multi-View High-Efficiency Video coding (MV-HEVC) encoder/decoder with a maximum resolution of 1088p. The latest MV-HEVC standard represents a significant improvement over the previous video coding standard (MVC). However, the MV-HEVC standard also has higher computational complexity. To this point, research using the MV-HEVC has had to use the Central Processing unit (CPU) on a Personal Computer (PC) or workstation for decompression, because MVHEVC is much more complex than High-Efficiency Video coding (HEVC), and because decompressors need higher parallelism to decompress in real time. It is particularly difficult to encode/decode in an embedded device. Therefore, we propose a novel framework for an MV-HEVC encoder/decoder that is based on a heterogeneously distributed embedded system. To this end, we use a parallel computing method to divide the video into multiple blocks and then code the blocks independently in each sub-work node with a group of pictures and a coding tree unit level. To appropriately assign the tasks to each work node, we propose a new allocation method that makes the operation of the entire heterogeneously distributed system more efficient. Our experimental results show that, compared to the single device (3D-HTM single threading), the proposed distributed MV-HEVC decoder and encoder performance increased approximately (20.39 and 68.7) times under 20 devices (multithreading) with the CTU level of a 1088p resolution video, respectively. Further, at the proposed GOP level, the decoder and encoder performance with 20 devices (multithreading) respectively increased approximately (20.78 and 77) times for a 1088p resolution video with heterogeneously distributed computing compared to the single device (3D-HTM single threading).
High Efficiency Video coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The coding tree unit (CTU) is the main buildi...
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High Efficiency Video coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The coding tree unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 x 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as codingunits (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video coding (VVC) and AOMedia Video 1 (AV1).
In High Efficiency Video coding (HEVC), the coding efficiency of I-frames is lower than P-frames and B-frames, which will cause the flicker artifact, especially in low bitrates applications. We propose a region-classi...
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ISBN:
(纸本)9781479923427
In High Efficiency Video coding (HEVC), the coding efficiency of I-frames is lower than P-frames and B-frames, which will cause the flicker artifact, especially in low bitrates applications. We propose a region-classification-based rate control for coding tree units (CTUs) in I-frames to improve the reconstructed quality of I-frames to suppress the flicker artifact. The CTUs in I-frame are classified into three regions according to their motion vectors and complexity. When the bit budget of one I-frame is used up, the target bitrates for the remaining CTUs will be adjusted according to the regions they belong to, and the pixel-based unified rate-quantization (URQ) model is then used to calculate the QPs. Experimental results demonstrate that the proposed scheme can efficiently suppress the flicker artifacts and improve both the subjective and objective video quality when compared with the original scheme in HM9.0.
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