Fast Coding Unit Size Decision Based on Probabilistic Graphical Model in High Efficiency Video Coding Inter Prediction

Jiang, Xiantao; Song, Tian; Shi, Wen Zhong; Katayama, Takafumi; Satō, Takashi; Wang, Lisheng

doi:10.1587/transinf.2015edl8237

Cited by 7 publications

(5 citation statements)

References 5 publications

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“…Moreover, the increasing of the BDBR is smaller than state-of-the-art works, while the time saving is more than 50% on average. Compared with previous works [19,20], the proposed work can trade-off the encoding complexity and encoding efficiency successfully.…”

Section: Resultsmentioning

confidence: 94%

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Low-Complexity and Hardware-Friendly H.265/HEVC Encoder for Vehicular Ad-Hoc Networks

Jiang

Feng

Song

et al. 2019

Sensors

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Real-time video streaming over vehicular ad-hoc networks (VANETs) has been considered as a critical challenge for road safety applications. The purpose of this paper is to reduce the computation complexity of high efficiency video coding (HEVC) encoder for VANETs. Based on a novel spatiotemporal neighborhood set, firstly the coding tree unit depth decision algorithm is presented by controlling the depth search range. Secondly, a Bayesian classifier is used for the prediction unit decision for inter-prediction, and prior probability value is calculated by Gibbs Random Field model. Simulation results show that the overall algorithm can significantly reduce encoding time with a reasonably low loss in encoding efficiency. Compared to HEVC reference software HM16.0, the encoding time is reduced by up to 63.96%, while the Bjontegaard delta bit-rate is increased by only 0.76–0.80% on average. Moreover, the proposed HEVC encoder is low-complexity and hardware-friendly for video codecs that reside on mobile vehicles for VANETs.

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Section: Resultsmentioning

confidence: 94%

“…Jiang et al presented a fast encoding complexity method based on the probabilistic graphical model [19,20]. These proposed algorithms consist of CU early termination and CU early skip methods to reduce the redundant computing of inter-prediction in H.265/HEVC.…”

Section: Related Workmentioning

confidence: 99%

Low-Complexity and Hardware-Friendly H.265/HEVC Encoder for Vehicular Ad-Hoc Networks

Jiang

Feng

Song

et al. 2019

Sensors

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“…Modern video coding standards can be divided into the following categories: H.264/AVC, H.264/SVC, H.265/HEVC, AVS2 and AV1 [3][4][5]. At present, the encoder optimization algorithm based on the H.26X standard is widely studied, and it can reduce the video freeze time and computational complexity under the condition of ensuring video quality in complex CVIS environments.…”

Section: Traditional Video Application Methods Over Cvismentioning

confidence: 99%

Deep Learning for Video Application in Cooperative Vehicle-Infrastructure System: A Comprehensive Survey

Dai

2022

Applied Sciences

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Video application is a research hotspot in cooperative vehicle-infrastructure systems (CVIS) which is greatly related to traffic safety and the quality of user experience. Dealing with large datasets of feedback from complex environments is a challenge when using traditional video application approaches. However, the in-depth structure of deep learning has the ability to deal with high-dimensional data sets, which shows better performance in video application problems. Therefore, the research value and significance of video applications over CVIS can be better reflected through deep learning. Firstly, the research status of traditional video application methods and deep learning methods over CVIS were introduced; the existing video application methods based on deep learning were classified according to generative and discriminative deep architecture. Then, we summarized the main methods of deep learning and deep reinforcement learning algorithms for video applications over CVIS, and made a comparative study of their performances. Finally, the challenges and development trends of deep learning in the field were explored and discussed.

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“…In H.265/HEVC, the maximal size of the basic coding unit (CU) is 64 × 64, and the search range is a key parameter on search-quality control for motion estimation (ME). Compared with H.264/AVC, H.265/HEVC achieves about 50% bitrate saving, while computation complexity is significantly increased [2].…”

Section: Introductionmentioning

confidence: 99%

Spatial Correlation-Based Motion-Vector Prediction for Video-Coding Efficiency Improvement

et al. 2019

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H.265/HEVC achieves an average bitrate reduction of 50% for fixed video quality compared with the H.264/AVC standard, while computation complexity is significantly increased. The purpose of this work is to improve coding efficiency for the next-generation video-coding standards. Therefore, by developing a novel spatial neighborhood subset, efficient spatial correlation-based motion vector prediction (MVP) with the coding-unit (CU) depth-prediction algorithm is proposed to improve coding efficiency. Firstly, by exploiting the reliability of neighboring candidate motion vectors (MVs), the spatial-candidate MVs are used to determine the optimized MVP for motion-data coding. Secondly, the spatial correlation-based coding-unit depth-prediction is presented to achieve a better trade-off between coding efficiency and computation complexity for interprediction. This approach can satisfy an extreme requirement of high coding efficiency with not-high requirements for real-time processing. The simulation results demonstrate that overall bitrates can be reduced, on average, by 5.35%, up to 9.89% compared with H.265/HEVC reference software in terms of the Bjontegaard Metric.

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Fast Coding Unit Size Decision Based on Probabilistic Graphical Model in High Efficiency Video Coding Inter Prediction

Cited by 7 publications

References 5 publications

Low-Complexity and Hardware-Friendly H.265/HEVC Encoder for Vehicular Ad-Hoc Networks

Low-Complexity and Hardware-Friendly H.265/HEVC Encoder for Vehicular Ad-Hoc Networks

Deep Learning for Video Application in Cooperative Vehicle-Infrastructure System: A Comprehensive Survey

Spatial Correlation-Based Motion-Vector Prediction for Video-Coding Efficiency Improvement

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