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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Soulef Bouaafia ◽  
Randa Khemiri ◽  
Amna Maraoui ◽  
Fatma Elzahra Sayadi
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Rate Distortion ◽  
Temporal Correlation ◽  
Complexity Reduction ◽  
Video Frame ◽  
Search Rate ◽  
High Efficiency Video Coding ◽  
Decision Algorithm ◽  
Cu Partition

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.

scholarly journals Novel Intermode Prediction Algorithm for High Efficiency Video Coding Encoder

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chan-seob Park ◽  
Gwang-Soo Hong ◽  
Byung-Gyu Kim
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Rate Distortion ◽  
Experimental Result ◽  
Prediction Algorithm ◽  
Basic Unit ◽  
Test Model ◽  
High Efficiency Video Coding ◽  
Time Saving ◽  
Decision Algorithm

The joint collaborative team on video coding (JCT-VC) is developing the next-generation video coding standard which is called high efficiency video coding (HEVC). In the HEVC, there are three units in block structure: coding unit (CU), prediction unit (PU), and transform unit (TU). The CU is the basic unit of region splitting like macroblock (MB). Each CU performs recursive splitting into four blocks with equal size, starting from the tree block. In this paper, we propose a fast CU depth decision algorithm for HEVC technology to reduce its computational complexity. In2N×2N PU, the proposed method compares the rate-distortion (RD) cost and determines the depth using the compared information. Moreover, in order to speed up the encoding time, the efficient merge SKIP detection method is developed additionally based on the contextual mode information of neighboring CUs. Experimental result shows that the proposed algorithm achieves the average time-saving factor of 44.84% in the random access (RA) at Main profile configuration with the HEVC test model (HM) 10.0 reference software. Compared to HM 10.0 encoder, a small BD-bitrate loss of 0.17% is also observed without significant loss of image quality.

Run-Time Deep Learning Enhanced Fast Coding Unit Decision for High Efficiency Video Coding

2019 ◽  
Vol 29 (03) ◽  
pp. 2050046
Author(s):  
Xin Li ◽  
Na Gong
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Random Access ◽  
High Efficiency Video Coding ◽  
Decision Algorithm ◽  
Coding Efficiency ◽  
Low Delay ◽  
Cu Partition ◽  
Coding Unit

The state-of-the-art high efficiency video coding (HEVC/H.265) adopts the hierarchical quadtree-structured coding unit (CU) to enhance the coding efficiency. However, the computational complexity significantly increases because of the exhaustive rate-distortion (RD) optimization process to obtain the optimal coding tree unit (CTU) partition. In this paper, we propose a fast CU size decision algorithm to reduce the heavy computational burden in the encoding process. In order to achieve this, the CU splitting process is modeled as a three-stage binary classification problem according to the CU size from [Formula: see text], [Formula: see text] to [Formula: see text]. In each CU partition stage, a deep learning approach is applied. Appropriate and efficient features for training the deep learning models are extracted from spatial and pixel domains to eliminate the dependency on video content as well as on encoding configurations. Furthermore, the deep learning framework is built as a third-party library and embedded into the HEVC simulator to speed up the process. The experiment results show the proposed algorithm can achieve significant complexity reduction and it can reduce the encoding time by 49.65%(Low Delay) and 48.81% (Random Access) on average compared with the traditional HEVC encoders with a negligible degradation (2.78% loss in BDBR, 0.145[Formula: see text]dB loss in BDPSNR for Low Delay, and 2.68% loss in BDBR, 0.128[Formula: see text]dB loss in BDPSNR for Random Access) in the coding efficiency.

scholarly journals Adaptive CU Split Decision Based on Deep Learning and Multifeature Fusion for H.266/VVC

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jinchao Zhao ◽  
Yihan Wang ◽  
Qiuwen Zhang
Keyword(s):  
Deep Learning ◽  
Video Coding ◽  
High Efficiency ◽  
Texture Classification ◽  
Rate Distortion ◽  
Classification Model ◽  
High Efficiency Video Coding ◽  
Fast Encoding ◽  
Training Samples ◽  
Coding Unit

With the development of technology, the hardware requirement and expectations of user for visual enjoyment are getting higher and higher. The multitype tree (MTT) architecture is proposed by the Joint Video Experts Team (JVET). Therefore, it is necessary to determine not only coding unit (CU) depth but also its split mode in the H.266/Versatile Video Coding (H.266/VVC). Although H.266/VVC achieves significant coding performance on the basis of H.265/High Efficiency Video Coding (H.265/HEVC), it causes significantly coding complexity and increases coding time, where the most time-consuming part is traversal calculation rate-distortion (RD) of CU. To solve these problems, this paper proposes an adaptive CU split decision method based on deep learning and multifeature fusion. Firstly, we develop a texture classification model based on threshold to recognize complex and homogeneous CU. Secondly, if the complex CUs belong to edge CU, a Convolutional Neural Network (CNN) structure based on multifeature fusion is utilized to classify CU. Otherwise, an adaptive CNN structure is used to classify CUs. Finally, the division of CU is determined by the trained network and the parameters of CU. When the complex CUs are split, the above two CNN schemes can successfully process the training samples and terminate the rate-distortion optimization (RDO) calculation for some CUs. The experimental results indicate that the proposed method reduces the computational complexity and saves 39.39% encoding time, thereby achieving fast encoding in H.266/VVC.

scholarly journals Learned Video Compression via Joint Spatial-Temporal Correlation Exploration

2020 ◽  
Vol 34 (07) ◽  
pp. 11580-11587
Author(s):  
Haojie Liu ◽  
Han Shen ◽  
Lichao Huang ◽  
Ming Lu ◽  
Tong Chen ◽  
...  
Keyword(s):  
Video Coding ◽  
Video Compression ◽  
High Efficiency ◽  
Temporal Correlation ◽  
Second Order ◽  
Compression Method ◽  
High Efficiency Video Coding ◽  
First Order ◽  
Coding Efficiency ◽  
The Common

Traditional video compression technologies have been developed over decades in pursuit of higher coding efficiency. Efficient temporal information representation plays a key role in video coding. Thus, in this paper, we propose to exploit the temporal correlation using both first-order optical flow and second-order flow prediction. We suggest an one-stage learning approach to encapsulate flow as quantized features from consecutive frames which is then entropy coded with adaptive contexts conditioned on joint spatial-temporal priors to exploit second-order correlations. Joint priors are embedded in autoregressive spatial neighbors, co-located hyper elements and temporal neighbors using ConvLSTM recurrently. We evaluate our approach for the low-delay scenario with High-Efficiency Video Coding (H.265/HEVC), H.264/AVC and another learned video compression method, following the common test settings. Our work offers the state-of-the-art performance, with consistent gains across all popular test sequences.

A Fast Mode Decision Algorithm for Intra Prediction in High Efficiency Video Coding

2020 ◽  
Vol 10 (2) ◽  
pp. 496-501
Author(s):  
Wen Si ◽  
Qian Zhang ◽  
Zhengcheng Shi ◽  
Bin Wang ◽  
Tao Yan ◽  
...  
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Rate Increase ◽  
Intra Prediction ◽  
High Efficiency Video Coding ◽  
Candidate List ◽  
Decision Algorithm ◽  
Coding Efficiency ◽  
Gradient Based ◽  
Coding Unit

High Efficiency Video Coding (HEVC) is the next generation video coding standard. In HEVC, 35 intra prediction modes are defined to improve coding efficiency, which result in huge computational complexity, as a large number of prediction modes and a flexible coding unit (CU) structure is adopted in CU coding. To reduce this computational burden, this paper presents a gradient-based candidate list clipping algorithm for Intra mode prediction. Experimental results show that the proposed algorithm can reduce 29.16% total encoding time with just 1.34% BD-rate increase and –0.07 dB decrease of BD-PSNR.

scholarly journals Enhanced Intra Prediction Based on Adaptive Coding Order and Multiple Reference Sets in HEVC

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 703
Author(s):  
Jin Young Lee
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Prediction Method ◽  
Rate Distortion ◽  
Spatial Prediction ◽  
Intra Prediction ◽  
High Efficiency Video Coding ◽  
Adaptive Coding ◽  
Advanced Video Coding ◽  
Multiple Reference

High Efficiency Video Coding (HEVC) is the most recent video coding standard. It can achieve a significantly higher coding performance than previous video coding standards, such as MPEG-2, MPEG-4, and H.264/AVC (Advanced Video Coding). In particular, to obtain high coding efficiency in intra frames, HEVC investigates various directional spatial prediction modes and then selects the best prediction mode based on rate-distortion optimization. For further improvement of coding performance, this paper proposes an enhanced intra prediction method based on adaptive coding order and multiple reference sets. The adaptive coding order determines the best coding order for each block, and the multiple reference sets enable the block to be predicted from various reference samples. Experimental results demonstrate that the proposed method achieves better intra coding performance than the conventional method.

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