Hierarchical Complexity Control of HEVC for Live Video Encoding

Deng, Xin; Xu, Mai; Li, Chen

doi:10.1109/access.2016.2612691

Cited by 11 publications

(11 citation statements)

References 21 publications

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“…To evaluate the performance of the proposed algorithm more intuitively, we compared our algorithm with three state-of-the-art algorithms [14,16,17]. The results are listed in Tables 5, 6, and 7.…”

Section: Resultsmentioning

confidence: 99%

“…In the case of complexity control algorithms, the rate distortion performance of video sequences severely deteriorates as the target encoding time decreases [9][10][11][12][13][14][15]. Moreover, the coding time of each frame in one GOP differs significantly because of different coding parameters.…”

Section: Complexity Allocation and Control Of Gop Layer And Frame Layermentioning

confidence: 99%

“…In [14], it has been shown that the greater the corresponding bit of the current CU, the greater is the probability that it is not optimal partition. For further analyzing the relationship between bit of the current CU and the probability that it is not optimal partition, we used the same experimental environment and training sequences described in Section 3.3.…”

Section: Adaptive Upper and Lower Bit Threshold Decisionmentioning

confidence: 99%

“…Further, they studied the relationship between the maximal depth and the complexity and limited the maximal CTU depth by combining the temporal correlation and visual weight. Their algorithm not only controls the computational complexity but also guarantees the subjective and objective quality [14]. In addition, they have proposed a complexity control algorithm for video conferencing, which is adaptable to the features of video conferencing [15].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, they have proposed a complexity control algorithm for video conferencing, which is adaptable to the features of video conferencing [15]. The abovementioned methods of complexity allocation [13][14][15] are more effective for sequences with less texture, while they result in degradation of the rate distortion performance for sequences with rich texture. Zhang et al established a statistical model to estimate the complexity of CTU coding and restricted the CTU depth traversal range to achieve complexity control.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Hierarchical complexity control algorithm for HEVC based on coding unit depth decision

Chen

Wen

Peng

et al. 2018

J Image Video Proc.

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The next-generation High Efficiency Video Coding (HEVC) standard reduces the bit rate by 44% on average compared to the previous-generation H.264 standard, resulting in higher encoding complexity. To achieve normal video coding in power-constrained devices and minimize the rate distortion degradation, this paper proposes a hierarchical complexity control algorithm for HEVC on the basis of the coding unit depth decision. First, according to the target complexity and the constantly updated reference time, the coding complexity of the group of pictures layer and the frame layer is allocated and controlled. Second, the maximal depth is adaptively assigned to the coding tree unit (CTU) on the basis of the correlation between the residual information and the optimal depth by establishing the complexity-depth model. Then, the coding unit smoothness decision and adaptive low bit threshold decision are proposed to constrain the unnecessary traversal process within the maximal depth assigned by the CTU. Finally, adaptive upper bit threshold decision is used to continue the necessary traversal process at a larger depth than the maximal depth of allocation to guarantee the quality of important coding units. Experimental results show that our algorithm can reduce the encoding time by up to 50%, with notable control precision and limited performance degradation. Compared to state-of-the-art algorithms, the proposed algorithm can achieve higher control accuracy.

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Section: Resultsmentioning

confidence: 99%