An efficient intra coding algorithm based on statistical learning for screen content coding

Yang, Hao; Shen, Liquan; An, Ping

doi:10.1109/icip.2017.8296726

Cited by 17 publications

(39 citation statements)

References 12 publications

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“…Since the fast prediction rules in [24]- [28] do not utilize the information from the decoder side, and they rely on the assumption that the SCBs are noise-free, they show poor performance compared with the proposed CNN-BRT. Specifically, CBFT+Zhang [24], CBFT+Duanmu [25], CBFT+Lei [26], CBFT+Yang [27], and CBFT+Kuang [28] provides 31.83%, 30.15%, 31.61%, 22.00% and 41.62% with BDBR increased by 1.65%, 6.08%, 2.34%, 2.38%, and 4.59% on average, respectively. Although the fast bitrate transcoder of Zhang et al [29] utilizes both the decoder side information and the encoder side information, it shows inferior performance than the proposed CNN-BRT because it only derives limited hand-crafted rules to speed up the re-encoding process.…”

Section: B Performance Evaluation Of Cnn-brtmentioning

confidence: 94%

“…Then, the bit cost and the depth information from the temporal and spatial neighboring CUs are utilized to early terminate CU partitions. In [27], Intra mode is firstly searched for all CUs with 2N×2N prediction units (PUs) to collect some features. Based on these features and CU content, incoming CUs are classified into partitioning CUs and non-partitioning CUs.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Intra Bitrate Transcoding for Screen Content Coding Based on Convolutional Neural Network

Chan

Tsang

2019

IEEE Access

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The Screen Content Coding (SCC) extension of High Efficiency Video Coding (HEVC) is developed to improve the coding efficiency of screen content videos. To meet the diverse network requirement of different clients, bitrate transcoding for SCC is desired. This problem can be solved by a conventional brute-force transcoder (CBFT) which concatenates an original decoder and an original encoder. However, it induces high computational complexity associated with the re-encoding part of CBFT. This paper presents a convolutional neural network based bitrate transcoder (CNN-BRT) for SCC. By utilizing information from both the decoder side and the encoder side, CNN-BRT makes a fast prediction for all coding units (CUs) of a coding tree unit (CTU) in a single test. At the decoder side, decoded optimal mode maps that reflect the optimal modes and CU partitions in a CTU is derived. At the encoder side, the raw samples in a CTU are collected. Then, they are fed to CNN-BRT to make a fast prediction. To imitate the optimal mode selection in the original re-encoding part, CNN-BRT involves a loss function that takes both of the sub-optimal modes and the final optimal modes into consideration. Compared with the HEVC-SCC reference software SCM-3.0, the proposed CNN-BRT reduces encoding time by 54.86% on average with a negligible Bjøntegaard delta bitrate increase of 1.01% under all-intra configuration. INDEX TERMS Transcoding, screen content coding (SCC), fast algorithm, convolutional neural network.

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Section: B Performance Evaluation Of Cnn-brtmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Efficient Intra Bitrate Transcoding for Screen Content Coding Based on Convolutional Neural Network

Chan

Tsang

2019

IEEE Access

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“…To reduce the computational complexity of a HEVC to SCC transcoder, one possible way is to use various fast encoding algorithms [24]- [26] to replace the original SCC encoder of CBFT in Fig. 1 by utilizing SCC encoder side information only.…”

Section: Introductionmentioning

confidence: 99%

“…However, it is not suitable for screen content videos with many dynamic regions. In [25] and [26], algorithms were proposed to make both fast mode decision and fast CU size decision. In [25], it classifies CUs into NIBs and SCBs by content analysis.…”

Section: Introductionmentioning

confidence: 99%

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Fast HEVC to SCC Transcoder by Early CU Partitioning Termination and Decision Tree-Based Flexible Mode Decision for Intra-Frame Coding

2019

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Screen content coding (SCC) was developed to enhance High Efficiency Video Coding (HEVC) for encoding screen content videos. However, HEVC has dominated the market for many years, and it leaves many legacy screen content videos encoded by HEVC. Therefore, it is desired that the legacy screen content videos are migrated from HEVC to SCC to improve the coding efficiency. This paper presents a fast transcoding algorithm by analyzing various features from four categories. They are the features from the HEVC decoder, static features, dynamic features, and spatial features. First, the coding unit (CU) depth level collected from the HEVC decoder is utilized to early terminate the CU partition in SCC. Second, a flexible encoding structure is proposed to make early mode decisions with the help of various features. On the one hand, high decision accuracy is achieved because mode decision is considered from different aspects by utilizing features from more than one category. On the other hand, high computational complexity is reduced because the flexible structure considers the decision of each mode separately. The experimental results show that the proposed algorithm provides 51.24% and 54.65% re-encoding time reduction with 1.32% and 1.25% negligible Bjøntegaard delta bitrate loss for YUV 4:2:0 and YUV 4:4:4 screen content sequences using all-intra configuration, respectively. INDEX TERMS Transcoding, screen content coding (SCC), High Efficiency Video Coding (HEVC), fast algorithm, machine learning.

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A support vector machine based fast planar prediction mode decision algorithm for versatile video coding

Yao

Wang

et al. 2022

Multimed Tools Appl

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An efficient intra coding algorithm based on statistical learning for screen content coding

Cited by 17 publications

References 12 publications

Efficient Intra Bitrate Transcoding for Screen Content Coding Based on Convolutional Neural Network

Efficient Intra Bitrate Transcoding for Screen Content Coding Based on Convolutional Neural Network

Fast HEVC to SCC Transcoder by Early CU Partitioning Termination and Decision Tree-Based Flexible Mode Decision for Intra-Frame Coding

A support vector machine based fast planar prediction mode decision algorithm for versatile video coding

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