Machine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis

Lin, Hongwei; He, Xiaohai; Qing, Linbo; Su, Shan; Xiong, Shengwu

doi:10.1049/iet-ipr.2018.5703

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…The study is validated over [44] have constructed a classifier using tree concept and Bayesian approach. The prime intention of the study was to forecast the depth of coding unit in HEVC using a unique feature selection method.…”

Section: B Typical Machine Learning Approachmentioning

confidence: 99%

Insights on Video Compression Strategies using Machine Learning

Rao

2019

IJEAT

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With the rising advancement of the multimedia technology, video compression is becoming a challenging problem. Although, there is availability of various standard compression algorithms, yet robust compression performance is yet to be seen in existing compression techniques. This paper also highlights that machine learning plays a significant contributory role in improving the performance of the video compression. Therefore, this manuscript offers a technical insight about the performance of existing video compression technique using machine learning approach. The contribution of this paper is its findings which states that machine learning approach do have significant advantage but the advantageous features are limited by the inherent and unsolved research problem. The core findings of this paper are basically to highlight the strength and limitations of existing methods as well as to highlight the research gap in terms of open-end research problems which requires immediate attention.

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Section: B Typical Machine Learning Approachmentioning

confidence: 99%

Insights on Video Compression Strategies using Machine Learning

Rao

2019

IJEAT

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“…Although the encoding performance could be improved by various encoding schemes and the improved prediction method, the computational complexity is dramatically increased compared to H.264/ AVC. [6][7][8] Among the techniques used in HEVC encoding, inter prediction takes up to 89.1% of the total encoding complexity. In inter prediction, motioncompensated prediction using multiple reference pictures is used to improve coding efficiency, but has high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Complexity reduction method for High Efficiency Video Coding encoding based on scene-change detection and image texture information

Lee

Ahn

Kim

et al. 2019

International Journal of Distributed Sensor Networks

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Recently, as demand for high-quality video and realistic media has increased, High Efficiency Video Coding has been standardized. However, High Efficiency Video Coding requires heavy cost in terms of computational complexity to achieve high coding efficiency, which causes problems in fast coding processing and real-time processing. In particular, High Efficiency Video Coding inter-coding has heavy computational complexity, and the High Efficiency Video Coding inter prediction uses reference pictures to improve coding efficiency. The reference pictures are typically signaled in two independent lists according to the display order, to be used for forward and backward prediction. If an event occurs in the input video, such as a scene change, the inter prediction performs unnecessary computations. Therefore, the reference picture list should be reconfigured to improve the inter prediction performance and reduce computational complexity. To address this problem, this article proposes a method to reduce computational complexity for fast High Efficiency Video Coding encoding using information such as scene changes obtained from the input video through preprocessing. Furthermore, reference picture lists are reconstructed by sorting the reference pictures by similarity to the current coded picture using Angular Second Moment, Contrast, Entropy, and Correlation, which are image texture parameters from the input video. Simulations are used to show that both the encoding time and coding efficiency could be improved simultaneously by applying the proposed algorithms.

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Machine learning-based fast transcoding for low-power video communication in Internet of Things

Jiang

Yang

et al. 2022

SIViP

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Machine learning‐based H.264/AVC to HEVC transcoding via motion information reuse and coding mode similarity analysis

Cited by 3 publications

References 23 publications

Insights on Video Compression Strategies using Machine Learning

Insights on Video Compression Strategies using Machine Learning

Complexity reduction method for High Efficiency Video Coding encoding based on scene-change detection and image texture information

Machine learning-based fast transcoding for low-power video communication in Internet of Things

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