Reduction of intra-coding time for HEVC based on temporary direction map

Heidari, Behnam; Ramezanpour, Mohammadreza

doi:10.1007/s11554-018-0815-7

Cited by 11 publications

(5 citation statements)

References 27 publications

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“…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

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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.

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Section: Discussionmentioning

confidence: 99%

Enhanced Intra String Copy for Screen Content Coding in AVS3

et al. 2022

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“…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

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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.

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“…Moreover, focusing on 3D-HEVC, Li et al [15] proposed a self-learning residual model -based fast CU size decision approach for the intra-coding of both texture views and depth maps. In [8], an intra-prediction technique was proposed to improve the performance of the HEVC standard by minimizing its computational complexity.…”

Section: Related Workmentioning

confidence: 99%

“…To address the above mentioned problem (i.e., the heavy burden caused by the searching for the optimal combination of CU sizes), many works designed fast CU size decision algorithms, which can be roughly classified to two main categories [5,6]. The first category includes the algorithms based on statistics-based heuristics [7,8]. The second category consists of the algorithms based on advanced machine learning techniques, such as Support Vector Machine (SVM) [9], Decision Trees [10], Bayesian method with conditional random fields [11] and Neural Networks [12,13].…”

Section: Introductionmentioning

confidence: 99%

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

Peng

et al. 2020

Electronics

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High Efficiency Video Coding (HEVC) has achieved about 50% bit-rates saving compared with its predecessor H.264 standard, while the encoding complexity increases dramatically. Due to the introduction of more flexible partition structures and more optional prediction directions, HEVC takes a brute force approach to find the optimal partitioning result which is much more time consuming. Therefore, this paper proposes a bagged trees based fast approach (BTFA) and focuses on the coding unit (CU) size decision for HEVC intra-coding. First, several key features of a target CU are extracted for three-output classifiers. Then, to avoid feature extraction and prediction time over head, our approach is designed frame-wisely, and the procedure is applied parallel with the encoding process. Using the adaptive threshold determination algorithm, our approach achieves 42.04% time saving with negligible 0.92% Bit-Distortion (BD)-rate loss. Furthermore, in order to calculate the optimal thresholds to balance BD-rate loss and complexity reduction, the neural network based mathematical fitting is added to BTFA, which is called the advanced bagged trees based fast approach (ABTFA). Finally, experimental results show that ABTFA achieves 47.87% time saving with only 0.96% BD-rate loss, which outperforms other state-of-the-art approaches.

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Reduction of intra-coding time for HEVC based on temporary direction map

Cited by 11 publications

References 27 publications

Enhanced Intra String Copy for Screen Content Coding in AVS3

Enhanced Intra String Copy for Screen Content Coding in AVS3

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

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

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