A fast SAO algorithm based on coding unit partition for HEVC

The high-efficiency video coding (HEVC) standard incorporates sample adaptive offset (SAO) as an in-loop filtering technique to reduce distortion. The fundamental tenet of SAO is to classify every reconstructed pixel and then simply add the offset to each group of pixels. The previous existing technique employs 32 bands in band offset (BO) calculation to determine the proper offset value to minimize the distortion in the SAO estimate algorithm for statistics collection and parametric determination procedure. The suggested method will reduce the necessary band count by 16 bands. It does not alter how edge offset is determined in any way. It offers enough and efficient room for creating superior band group predictions and selections, as well as an ideal decrease in the implemented area. Thus, reducing the number of bands to 16 also decreases the amount of storage needed to hold each pixel’s information in binary files by up to 0.65%.

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“…Te Bjøntegaard model was a popular evaluating tool to defne coding efciency which was used here to calculate average bit-rate saving [23,24].…”

Section: Parameter Analysismentioning

confidence: 99%

Optimized Sample Adaptive Offset Filter in HEVC

Archana¹,

Menaka²,

Raj

et al. 2022

Advances in Multimedia

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“…This is further corroborated by the experimental results presented in [20] and [21] for their fast motion estimation methods that report ≈ 20% encoding time reductions. Similarly, fast Sample Adaptive Offset (SAO) parameter estimation algorithms proposed in [22] and [23] are limited to average encoding time gains that are in the range of 42% − 45%.…”

Section: B Related Workmentioning

confidence: 99%

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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“…Furthermore, they also proposed to make a simplified decision of the best SAO edge offset pattern by using the dominant edge direction [27], which reduced the RDO calculation and sped up the SAO encoding process in HEVC. Zhang et al [28] proposed to distinguish videos according to texture complexity and performed an adaptive offset process by reducing some unnecessary sample offsets to improve the video coding efficiency. Gendy et al [29] proposed an algorithm to reduce the complexity of SAO parameter estimation by adaptively reusing the dominant mode of corresponding set of CTUs, which saved SAO encoding time.…”

Section: Related Workmentioning

confidence: 99%

“…The comparison shows that the proposed method can effectively reduce the SAO encoding time in the case of a small BD-rate performance loss for VSNs. Table 6 compares the proposed method with the other three methods in [27,28,30] under AI configuration. It can be seen that the proposed method averagely achieves 63.68% SAO encoding time saving, which is better than the methods in [27] (53.43%), [28] (9.62%), and [30] (48.34%).…”

Section: ∆T =mentioning

confidence: 99%

“…Table 6 compares the proposed method with the other three methods in [27,28,30] under AI configuration. It can be seen that the proposed method averagely achieves 63.68% SAO encoding time saving, which is better than the methods in [27] (53.43%), [28] (9.62%), and [30] (48.34%). Compared with the method in [27], it can be seen that the proposed algorithm achieves further computational complexity reduction with a much smaller increment in the BD-rate.…”

Section: ∆T =mentioning

confidence: 99%

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Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks

Wang

Tang

2020

Sensors

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Visual sensor networks (VSNs) can be widely used in multimedia, security monitoring, network camera, industrial detection, and other fields. However, with the development of new communication technology and the increase of the number of camera nodes in VSN, transmitting and compressing the huge amounts of video and image data generated by video and image sensors has become a major challenge. The next-generation video coding standard—versatile video coding (VVC), can effectively compress the visual data, but the higher compression rate is at the cost of heavy computational complexity. Therefore, it is vital to reduce the coding complexity for the VVC encoder to be used in VSNs. In this paper, we propose a sample adaptive offset (SAO) acceleration method by jointly considering the histogram of oriented gradient (HOG) features and the depth information for VVC, which reduces the computational complexity in VSNs. Specifically, first, the offset mode selection (select band offset (BO) mode or edge offset (EO) mode) is simplified by utilizing the partition depth of coding tree unit (CTU). Then, for EO mode, the directional pattern selection is simplified by using HOG features and support vector machine (SVM). Finally, experimental results show that the proposed method averagely saves 67.79% of SAO encoding time only with 0.52% BD-rate degradation compared to the state-of-the-art method in VVC reference software (VTM 5.0) for VSNs.

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A fast SAO algorithm based on coding unit partition for HEVC

Cited by 6 publications

References 7 publications

Optimized Sample Adaptive Offset Filter in HEVC

Optimized Sample Adaptive Offset Filter in HEVC

iCUS: Intelligent CU Size Selection for HEVC Inter Prediction

Fast Sample Adaptive Offset Jointly Based on HOG Features and Depth Information for VVC in Visual Sensor Networks

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