High Efficiency Video coding (HEVC) is an ongoing standard, and it employs the quad-tree block partitioning structure which includes codingunit, prediction unit, and transform unit. This content-adaptive coding tree ...
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ISBN:
(纸本)9783037856529
High Efficiency Video coding (HEVC) is an ongoing standard, and it employs the quad-tree block partitioning structure which includes codingunit, prediction unit, and transform unit. This content-adaptive coding tree structure can improve HEVC coding efficiency significantly, but it also consumes large computational complexity. This paper proposed a fast intra coding unit size decision algorithm to reduce the heavy complexity of HEVC encoding. First, the proposed algorithm reduced unitsizes search by using the classifier, which is based on the statistical learning. Second, an early largest unitsizedecision was designed to skip the checking of unnecessary unitsizes. As compared to the full search algorithm in HEVC reference software, experimental results show that the proposed algorithm achieves 50.4% computation saving on average with 1.83% bit rate increase and 0.070dB peak signal-to-noise ratio loss.
The advanced three-dimensional extension of high-efficiency video coding (3D-HEVC) is the latest coding standard for 3D video. The coding of the depth map for 3D-HEVC is very time-consuming. With the development of de...
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The advanced three-dimensional extension of high-efficiency video coding (3D-HEVC) is the latest coding standard for 3D video. The coding of the depth map for 3D-HEVC is very time-consuming. With the development of deep learning, it has become feasible to employ convolutional neural networks (CNNs) to predict the codingunit (CU) division of the depth map. However, there are three types of CU sizes: 64, 32, and 16, which makes it difficult to unify the model. The features of the depth map are very different from the texture map. In view of the aforementioned problems, we propose an adaptive CU size CNNs for fast 3D-HEVC depth map intracoding. We first employ spatial pyramid pooling to fully extract the features of the three types of CUs. Then, we apply the nonlocal self-attention mechanism to make it suitable for depth maps. Compared with the 3D-HEVC reference algorithm, the proposed network reduces the coding time by an average of 35.7%, while the quality degradation of the synthesized virtual view is negligible. (c) 2021 SPIE and IS&T [DOI: 10.1117/***.30.4.041405]
High efficiency video coding (HEVC) uses a quadtree-based structure for codingunit (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 video coding (HEVC) uses a quadtree-based structure for codingunit (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 sizedecision 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.
The high-efficiency video coding (HEVC) standard uses 35 intra-prediction modes for 2(N) x 2(N) (N is an integer number ranging from six to two) luma blocks and five modes for chroma blocks. To find the luma block wit...
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The high-efficiency video coding (HEVC) standard uses 35 intra-prediction modes for 2(N) x 2(N) (N is an integer number ranging from six to two) luma blocks and five modes for chroma blocks. To find the luma block with the minimum rate-distortion, it must perform 11935 different rate-distortion cost calculations. Although this approach improves coding efficiency compared to the previous standards such as H.264/AVC, computational complexity is increased significantly. In this paper, an intra-prediction technique has been described to improve the performance of the HEVC standard by minimizing its computational complexity. The proposed algorithm consists of two stages. The first stage, called prediction unitsizedecision (PUSD) was introduced to decrease evaluation of prediction unitsizes by ~ 38%. The second stage called prediction mode fast decision (PMFD) was developed to minimize the number of modes in the rough mode decision (RMD) stage. The simulation results show that the time complexity is decreased by ~ 47%, while the BD rate is increased by 1.08%, and PSNR is decreased by 0.04 db. Accordingly, the proposed algorithms have a negligible effect on the video quality with great saving in the time complexity.
The state-of-the-art high efficiency video coding (HEVC/H.265) adopts the hierarchical quadtree-structured codingunit (CU) to enhance the coding efficiency. However, the computational complexity significantly increas...
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The state-of-the-art high efficiency video coding (HEVC/H.265) adopts the hierarchical quadtree-structured codingunit (CU) to enhance the coding efficiency. However, the computational complexity significantly increases because of the exhaustive rate-distortion (RD) optimization process to obtain the optimal coding tree unit (CTU) partition. In this paper, we propose a fast CU sizedecision algorithm to reduce the heavy computational burden in the encoding process. In order to achieve this, the CU splitting process is modeled as a three-stage binary classification problem according to the CU size from 64x64, 32x32 to 16x16. In each CU partition stage, a deep learning approach is applied. Appropriate and efficient features for training the deep learning models are extracted from spatial and pixel domains to eliminate the dependency on video content as well as on encoding configurations. Furthermore, the deep learning framework is built as a third-party library and embedded into the HEVC simulator to speed up the process. The experiment results show the proposed algorithm can achieve significant complexity reduction and it can reduce the encoding time by 49.65%(Low Delay) and 48.81% (Random Access) on average compared with the traditional HEVC encoders with a negligible degradation (2.78% loss in BDBR, 0.145dB loss in BDPSNR for Low Delay, and 2.68% loss in BDBR, 0.128dB loss in BDPSNR for Random Access) in the coding efficiency.
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