Compared with the predecessor H.264/advanced video coding, high-efficiency video coding (hevc) is a new video coding standard with nearly double coding efficiency under the same coding quality. However, the computing ...
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Compared with the predecessor H.264/advanced video coding, high-efficiency video coding (hevc) is a new video coding standard with nearly double coding efficiency under the same coding quality. However, the computing complexity of hevc increases sharply. To solve this problem, a fast algorithm for intra prediction mode selection based on mode grouping is proposed by reducing the number of modes entering rough mode decision. Moreover, the dual support vector machine is proposed to efficiently select the coding unit (CU) size, which is based on texture features of CU and sub-CUs including content complexity and direction complexity. By using the new CU size selection algorithm, the encoder can confirm the split for complex CU and terminate the split for simple CU in advance, so as to reduce the computing complexity of CU size selection. The experimental results show that by employing the two fast algorithms in intracoding, it can save 42.80% encoding time, with 0.98% increment in bit rate and 0.018 dB loss of peak-signal-to-noise ratio of luminance, compared with the reference software x265-1.7.
High Efficiency Video coding (hevc) is the latest video coding standard released as a successor of H.264/AVC, it expected to reduce the bitrate by 50% for the same perceptual quality. One of the major contributors to ...
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ISBN:
(纸本)9781538668665
High Efficiency Video coding (hevc) is the latest video coding standard released as a successor of H.264/AVC, it expected to reduce the bitrate by 50% for the same perceptual quality. One of the major contributors to the higher compression performance of hevc is the introduction of larger coding Units (CU) with recursive partitioning mechanisms. This achievement in performance is accompanied by a high computational complexity, making this new standard very difficult to be embedded in current multimedia services and broadcast platforms. In this paper, a performance evaluation of All-intra (AI) parallel realization of hevc encoder is proposed, using a heterogeneous Octa-core CubieBoard4 platform that includes two quad-core ARM A7 and ARM A15. We used the OpenMP paradigm for parallel realization where each thread is assigned to a core processor to encode a separate frame. AI configuration is used to break coding dependencies between successive frames, which allow the parallel processing of a set of images. Experimental results shows that the proposed parallel realization of hevc encoder, using eight threads, reduces the computational complexity to about 4.35 x, without any loss in coding performance. These results do not match to the expected acceleration due to the heterogeneity of the platform.
The recent introduction of the High Efficiency Video coding (hevc) standard provides opportunities to improve medical image compression in picture archiving and communications systems. In this paper, we propose improv...
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ISBN:
(纸本)9781479928934
The recent introduction of the High Efficiency Video coding (hevc) standard provides opportunities to improve medical image compression in picture archiving and communications systems. In this paper, we propose improvements to the hevc intra coding process for lossless compression of grayscale anatomical medical images, which are characterized by their large amount of edges. Specifically, we propose alternative angular and planar prediction modes that are based on sample-wise differential pulse code modulation (DPCM) with an increased range of directionalities. We also propose an implementation of the DPCM decoding process that maintains the block-wise coding structure of hevc. Evaluation results on various medical images show that the proposed DPCM modes efficiently predict the large amount of edges in these images achieving bit-rate savings of up to 15%.
Task-driven semantic video/image coding has drawn considerable attention with the development of intelligent media applications, such as license plate detection, face detection, and medical diagnosis, which focuses on...
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Task-driven semantic video/image coding has drawn considerable attention with the development of intelligent media applications, such as license plate detection, face detection, and medical diagnosis, which focuses on maintaining the semantic information of videos/images. Deep neural network (DNN)-based codecs have been studied for this purpose due to their inherent end-to-end optimization mechanism. However, the traditional hybrid coding framework cannot be optimized in an end-to-end manner, which makes task-driven semantic fidelity metric unable to be automatically integrated into the rate-distortion optimization process. Therefore, it is still attractive and challenging to implement task-driven semantic coding with the traditional hybrid coding framework, which should still be widely used in practical industry for a long time. To solve this challenge, we design semantic maps for different tasks to extract the pixelwise semantic fidelity for videos/images. Instead of directly integrating the semantic fidelity metric into traditional hybrid coding framework, we implement task-driven semantic coding by implementing semantic bit allocation based on reinforcement learning (RL). We formulate the semantic bit allocation problem as a Markov decision process (MDP) and utilize one RL agent to automatically determine the quantization parameters (QPs) for different coding units (CUs) according to the task-driven semantic fidelity metric. Extensive experiments on different tasks, such as classification, detection and segmentation, have demonstrated the superior performance of our approach by achieving an average bitrate saving of 34.39% to 52.62% over the High Efficiency Video coding (H.265/hevc) anchor under equivalent task-related semantic fidelity.
The state-of-art high efficiency video coding (hevc) standard attracts much attention as its excellent performances in video compression in recent years. However the inherent coding complexity of hevc makes it difficu...
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The state-of-art high efficiency video coding (hevc) standard attracts much attention as its excellent performances in video compression in recent years. However the inherent coding complexity of hevc makes it difficult for real-time application. A fast algorithm for hevc intra coding based on online learning is proposed in this paper. During learning coding, the partition threshold of coding unit (CU) is obtained by learning the random sample set which conforms to Poisson probability distribution. Meanwhile the threshold of rate distortion cost is also obtained by online learning. After that, the acquired thresholds are used for early skip of CU depth or early termination of CU partition in the stage of fast coding. Anchoring the HM16 test model, the proposed algorithm achieves an important improvement of average time-saving 46.02%. In addition, it is also demonstrated that the proposed algorithm gains higher time efficiency when video sequence is encoded with higher compression efficiency. (C) 2018 Elsevier GmbH. All rights reserved.
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