Machine Learning Approaches for Intra-Prediction in HEVC

Erabadda, Buddhiprabha; Mallikarachchi, Thanuja; Kulupana, Gosala; Fernando, Anil

doi:10.1109/gcce.2018.8574648

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…The time spent during the encoding process to collect sequence-specific data hinders the encoding time reduction achieved in the latter. On the other hand, online-trained probabilistic models demonstrate less Bjøntegaard Delta Bit Rate (BDBR) [50] losses since the CU split decisions made in general are content-adaptive and relevant to the video content that is being encoded [49].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…However, it has been shown that offline trained generic models may not perform well with the dynamics of the video content. Thus, content specific CU split likelihood models are important to achieve a higher prediction accuracy [49]. Therefore, this section describes an online prediction model (that acts alongside the offline SVM prediction models) that can keep the CU split decision prediction content-adaptive.…”

Section: Cu Split Decision Classification Using Probabilistic Momentioning

confidence: 99%

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iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

et al. 2020

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The hierarchical quadtree partitioning of Coding Tree Units (CTU) is one of the striking features in HEVC that contributes towards its superior coding performance over its predecessors. However, the brute force evaluation of the quadtree hierarchy using the Rate-Distortion (RD) optimisation, to determine the best partitioning structure for a given content, makes it one of the most time-consuming operations in HEVC encoding. In this context, this paper proposes an intelligent fast Coding Unit (CU) size selection algorithm to expedite the encoding process of HEVC inter-prediction. The proposed algorithm introduces (i) two CU split likelihood modelling and classification approaches using Support Vector Machines (SVM) and Bayesian probabilistic models, and (ii) a fast CU selection algorithm that makes use of both offline trained SVMs and online trained Bayesian probabilistic models. Finally, (iii) a computational complexity to coding efficiency trade-off mechanism is introduced to flexibly control the algorithm to suit different encoding requirements. The experimental results of the proposed algorithm demonstrate an average encoding time reduction performance of 53.46%, 61.15%, and 58.15% for Low Delay B, Random Access, and Low Delay P configurations, respectively, with Bjøntegaard Delta-Bit Rate (BD-BR) losses of 2.35%, 2.9%, and 2.35%, respectively, when evaluated across a wide range of content types and quality levels. INDEX TERMS Coding Unit (CU), encoder complexity reduction, High Efficiency Video Coding (HEVC), inter-prediction, Support Vector Machine (SVM) R ECENT advancements in multimedia technologies that span across Consumer Electronics (CE) in video content capturing, transmission and display have made video data the most frequently exchanged type of content over the modern communication networks. The increasing mobile consumption of High Definition (HD) and Ultra High Definition (UHD) video contents has contributed immensely towards the ever-growing IP video traffic and it is expected to reach over 82% of the overall Internet traffic in 2021 [1]. However, the estimated growth in network bandwidth (1.9 fold from 2017-2022, which is 39.0 Mbps to 75.4 Mbps for fixed broadband [1]) with time is still not sufficient to cater for the ever-growing user demands. Furthermore, the video requirements for emerging applications such as Augmented Reality (AR)/Virtual Reality (VR), interactive television, multi-party video conferences and over-the-top (OTT) multimedia consumption demand continuous improvements in the compression efficiency [2]. In this regard, High Efficiency Video Coding (HEVC) which was introduced in 2013 is the most recent stable video coding standard. It provides greater compression efficiency through an assortment of new features and coding tools over its predecessor H.264/AVC [3]. Out of these, the hierarchical quadtree partitioning structure introduced in HEVC that entails a wide range of Coding Unit (CU) sizes (i.e., 8 × 8 to 64 × 64) and their combinations, is one of the important contributors...

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Cu Split Decision Classification Using Probabilistic Momentioning

confidence: 99%

iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

et al. 2020

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“…And the ration helps deciding the splitting decision of the CU. Buddhiprabha Erabadd et al (2018) [5] proposed a support vector machine-based solution to complexity reduction. They studied the online as well as offline model training of SVM and reported that off line training performs better in terms of time saving.…”

Section: Literature Surveymentioning

confidence: 99%

Intelligent Coding Unit Partitioning using Predictive Data Mining

Pawar*¹,

Sawarkar²

2019

IJITEE

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Increasing applications of videos in everyday life demands compressing the videos further. International bodies for Video Coding standards are working toward making it more efficient in terms of reducing bitrate so as to efficiently compress the high-resolution videos. With increasing resolution, the size of the Coding Units increases. Latest Video Coding techniques like High Efficiency Video Coding (HEVC) and Versatile Video coding (VVC) proposed Larger coding Units with flexible Quadtree decompositions. In Inter-picture prediction all the sub blocks have to find best partitioning structure during motion estimation. Due to larger coding units finding the best partitioning introduces computational complexity. In the proposed work we present a computational complexity control scheme using predictive data mining. The method helps to predict whether to split or no split the coding unit. The decision tree model trained offline in the proposed work achieves 77.73% saving in encoding time with minimal change of 0.15 in average PSNR and 0.00074 in average SSIM values.

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Rate Control in HEVC: A Survey

Zou

2019

Lecture Notes in Electrical Engineering

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Machine Learning Approaches for Intra-Prediction in HEVC

Cited by 6 publications

References 21 publications

iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

Intelligent Coding Unit Partitioning using Predictive Data Mining

Rate Control in HEVC: A Survey

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