Mass center direction-based decision method for intraprediction in HEVC standard

Najafabadi, Narjes; Ramezanpour, Mohammadreza

doi:10.1007/s11554-019-00864-z

Cited by 12 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since FoM must strike a good balance between more reduced encoding time and low BD-BR increase, lower FoM is desirable [ 41 ]. Table 5 displays the FoM s for the proposed algorithm and three different state-of-the-art algorithms.…”

Section: Experimental Results and Performance Analysismentioning

confidence: 99%

Fast CU Partition Algorithm for Intra Frame Coding Based on Joint Texture Classification and CNN

Wang,

Wei,

et al. 2023

Sensors

View full text Add to dashboard Cite

High-efficiency video coding (HEVC/H.265) is one of the most widely used video coding standards. HEVC introduces a quad-tree coding unit (CU) partition structure to improve video compression efficiency. The determination of the optimal CU partition is achieved through the brute-force search rate-distortion optimization method, which may result in high encoding complexity and hardware implementation challenges. To address this problem, this paper proposes a method that combines convolutional neural networks (CNN) with joint texture recognition to reduce encoding complexity. First, a classification decision method based on the global and local texture features of the CU is proposed, efficiently dividing the CU into smooth and complex texture regions. Second, for the CUs in smooth texture regions, the partition is determined by terminating early. For the CUs in complex texture regions, a proposed CNN is used for predictive partitioning, thus avoiding the traditional recursive approach. Finally, combined with texture classification, the proposed CNN achieves a good balance between the coding complexity and the coding performance. The experimental results demonstrate that the proposed algorithm reduces computational complexity by 61.23%, while only increasing BD-BR by 1.86% and decreasing BD-PSNR by just 0.09 dB.

show abstract

Section: Experimental Results and Performance Analysismentioning

confidence: 99%

Fast CU Partition Algorithm for Intra Frame Coding Based on Joint Texture Classification and CNN

Wang,

Wei,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Block-based video coding has been studied for half a century, whereas string-based video coding is still in its early stages. Future work includes 1) applying ISC to the nextgeneration video coding standard; 2) improving the coding performance of ISC in specific application scenarios [34] [35], especially in mobile wireless network application scenarios; 3) reducing the coding complexity with negligible coding performance [36], [37] of ISC; 4) applying deep learning and neural network [38] approaches to video analysis and ISC-based video coding.…”

Section: Discussionmentioning

confidence: 99%

Enhanced Intra String Copy for Screen Content Coding in AVS3

et al. 2022

View full text Add to dashboard Cite

Driven by growing applications that use computer screens as interfaces for daily remote interactions, almost all current video coding standards have included screen content coding (SCC) tools. Recently, an efficient SCC tool called intra string copy (ISC) has been adopted in the third-generation of audio video coding standard in China (AVS3). ISC has two coding unit (CU) level sub-modes: fully-matching-string and partially-matching-string based string prediction (FPSP) sub-mode and equal-value-string, unit-basis-vectorstring, and unmatched-pixel-string based string prediction (EUSP) sub-mode. To further improve the coding efficiency of SCC, this paper proposes four enhancement techniques of ISC (EISC), including CU partition improvements, point vector (PV) relocation and reactivation, line-based overlapping string prediction, and an optimized coding method for string length in the EUSP sub-mode. Compared with the latest AVS3 reference software HPM with EISC disabled, using AVS3 SCC common test condition and YUV test sequences in text and graphics with motion and mixed content categories, the proposed technique achieves an average Y BD-rate reduction of 2.39% and 1.49% for all intra (AI) and low-delay B (LDB) configurations, respectively, with low additional encoding complexity and almost no additional decoding complexity. All proposed ISC enhancement techniques have been adopted in AVS3.INDEX TERMS Video coding standard, screen content coding, intra string copy, string split, point vector.

show abstract

“…erefore, an algorithm can achieve a tradeoff between complexity reduction and RD efficiency; we have used two Figures of Merit (FoM), BD rate and ΔT, a common procedure of computing proposed in [31,32]: FoM � BD rate ΔT 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌 × 100.…”

Section: Comparative Performancementioning

confidence: 99%

CNN-LSTM Learning Approach-Based Complexity Reduction for High-Efficiency Video Coding Standard

Bouaafia

Khemiri

Amna

et al. 2021

Scientific Programming

View full text Add to dashboard Cite

High-Efficiency Video Coding provides a better compression ratio compared to earlier standard, H.264/Advanced Video Coding. In fact, HEVC saves 50% bit rate compared to H.264/AVC for the same subjective quality. This improvement is notably obtained through the hierarchical quadtree structured Coding Unit. However, the computational complexity significantly increases due to the full search Rate-Distortion Optimization, which allows reaching the optimal Coding Tree Unit partition. Despite the many speedup algorithms developed in the literature, the HEVC encoding complexity still remains a crucial problem in video coding field. Towards this goal, we propose in this paper a deep learning model-based fast mode decision algorithm for HEVC intermode. Firstly, we provide a deep insight overview of the proposed CNN-LSTM, which plays a kernel and pivotal role in this contribution, thus predicting the CU splitting and reducing the HEVC encoding complexity. Secondly, a large training and inference dataset for HEVC intercoding was investigated to train and test the proposed deep framework. Based on this framework, the temporal correlation of the CU partition for each video frame is solved by the LSTM network. Numerical results prove that the proposed CNN-LSTM scheme reduces the encoding complexity by 58.60% with an increase in the BD rate of 1.78% and a decrease in the BD-PSNR of -0.053 dB. Compared to the related works, the proposed scheme has achieved a best compromise between RD performance and complexity reduction, as proven by experimental results.

show abstract

Mass center direction-based decision method for intraprediction in HEVC standard

Cited by 12 publications

References 26 publications

Fast CU Partition Algorithm for Intra Frame Coding Based on Joint Texture Classification and CNN

Fast CU Partition Algorithm for Intra Frame Coding Based on Joint Texture Classification and CNN

Enhanced Intra String Copy for Screen Content Coding in AVS3

CNN-LSTM Learning Approach-Based Complexity Reduction for High-Efficiency Video Coding Standard

Contact Info

Product

Resources

About