Fast prediction for quality scalability of High Efficiency Video Coding Scalable Extension

Yeh, Chih Hsuan; Lin, Jie; Chen, Mei-Juan; Yeh, Chia Hung; Lee, Cheng An; Tai, Kuang Han

doi:10.1016/j.jvcir.2018.12.021

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Yeh et al [26] propose a coding unit complexity (CUC)based prediction method to improve the coding speed of HEVC-to-SHVC transcoding for quality scalability. Yeh et al [27] use encoded Coding Unit (CU) sizes, prediction modes, motion vectors and Rate-Distortion Costs (RD-Costs) in BL and encoded CU sizes in EL to accelerate the coding speed of quality SHVC. Shen et al [28] use conditional probability of a SKIP/Merge mode, motion activity and mode complexity to describe the video content to predict the EL mode decision to improve the coding speed.…”

Section: Related Workmentioning

confidence: 99%

Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding

Wang

Sun

Zhu

et al. 2020

IEEE Trans. Multimedia

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Section: Related Workmentioning

confidence: 99%

Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding

Wang

Sun

Zhu

et al. 2020

IEEE Trans. Multimedia

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“…The FRUC is not new and in fact has been in used in the past. To the best of our knowledge, at least there are two types of FRUC in place [7], [8]. The first type makes use of the non-motion-compensated (NMC) scheme, while the second one utilizes the motioncompensated (MC) concept.…”

Section: Introductionmentioning

confidence: 99%

An Elastic Frame Rate Up-Conversion for Sequential Omnidirectional Images

Satyawan¹,

Prini²,

Abu-Bakar³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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A distinct innovation in the development of frame rate up-conversion for an omnidirectional video is presented in this paper. The innovation allows an omnidirectional video to decide the number of omnidirectional frames that the main algorithm can create based on the characteristic of the motion of objects in the omnidirectional video itself. This elastic omnidirectional frame production is achievable since the main algorithm works based on an optical flow method with a self-improvement mechanism. The optical flow concept has proven to be adaptable greatly with the character of such a video. The experiment results, in which ten omnidirectional videos were involved, show that the algorithm's exertion and adaptability named the elastic frame rate up-conversion (E-FRUC) are incredible. This achievement is confirmed by both the excellent visual quality and the high score of the PSNR (33 to 59 dB) of the reconstructed omnidirectional frame (RF). The E-FRUC can generate at least one RF from two consecutive omnidirectional images, although these images are synthetically designed with an ideal motion. The more complex the motion objects condition inside the omnidirectional video is, the more RFs the E-FRUC can create. By using the E-FRUC, the continuity of motion of moving objects can be preserved. Therefore it can be useful to maintain the frame rate of incomplete omnidirectional video frames when such video is played back. Such a condition usually happens when the omnidirectional video is transmitted through error-prone telecommunication networks.

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A Deep Learning Approach in Scalable High Efficiency Video Coding for Fast Coding Unit Size Decision

Sairam

Muralidhar

2022

IETE Technical Review

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Fast prediction for quality scalability of High Efficiency Video Coding Scalable Extension

Cited by 5 publications

References 25 publications

Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding

Fast Depth and Inter Mode Prediction for Quality Scalable High Efficiency Video Coding

An Elastic Frame Rate Up-Conversion for Sequential Omnidirectional Images

A Deep Learning Approach in Scalable High Efficiency Video Coding for Fast Coding Unit Size Decision

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