scholarly journals Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning

2021 ◽  
Vol 9 (11) ◽  
pp. 1279
Author(s):  
Hongrui Lu ◽  
Yingjun Zhang ◽  
Zhuolin Wang
Keyword(s):  
Deep Learning ◽  
Digital Video ◽  
High Efficiency ◽  
Rate Distortion ◽  
Depth Interval ◽  
High Efficiency Video Coding ◽  
Fast Encoding ◽  
Radar Images ◽  
Rate Distortion Cost ◽  
Screen Sharing

The High Efficiency Video Coding Standard (HEVC) is one of the most advanced coding schemes at present, and its excellent coding performance is highly suitable for application in the navigation field with limited bandwidth. In recent years, the development of emerging technologies such as screen sharing and remote control has promoted the process of realizing the virtual driving of unmanned ships. In order to improve the transmission and coding efficiency during screen sharing, HEVC proposes a new extension scheme for screen content coding (HEVC-SCC), which is based on the original coding framework. SCC has improved the performance of compressing computer graphics content and video by adding new coding tools, but the complexity of the algorithm has also increased. At present, there is no delay in the compression optimization method designed for radar digital video in the field of navigation. Therefore, our paper starts from the perspective of increasing the speed of encoded radar video, and takes reducing the computational complexity of the rate distortion cost (RD-cost) as the goal of optimization. By analyzing the characteristics of shipborne radar digital video, a fast encoding algorithm for shipborne radar digital video based on deep learning is proposed. Firstly, a coding tree unit (CTU) division depth interval dataset of shipborne radar images was established. Secondly, in order to avoid erroneously skipping of the intra block copy (IBC)/palette mode (PLT) in the coding unit (CU) division search process, we designed a method to divide the depth interval by predicting the CTU in advance and limiting the CU rate distortion cost to be outside the traversal calculation depth interval, which effectively reduced the compression time. The effect of radar transmission and display shows that, within the acceptable range of Bjøntegaard Delta Bit Rate (BD-BR) and Bjøntegaard Delta Peak Signal to Noise Rate (BD-PSNR) attenuation, the algorithm proposed in this paper reduces the coding time by about 39.84%, on average, compared to SCM8.7.

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 Fast HEVC Intramode Decision Based on Hybrid Cost Ranking

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Liu ◽  
Yuexin Jie
Keyword(s):  
High Efficiency ◽  
Rate Distortion ◽  
Potential Candidate ◽  
Conditional Probabilities ◽  
High Efficiency Video Coding ◽  
Rate Distortion Cost ◽  
Real Time Applications ◽  
Optimal Mode ◽  
Improve Rate ◽  
Rate Loss

To improve rate-distortion (R-D) performance, high efficiency video coding (HEVC) increases the intraprediction modes with heavy computational load, and thus the intracoding optimization is highly demanded for real-time applications. According to the conditional probabilities of most probable modes and the correlation of potential candidate subsets, this paper proposes a fast HEVC intramode decision scheme based on the hybrid cost ranking which includes both Hadamard cost and rate-distortion cost. The proposed scheme utilizes the coded results of the modified rough mode decision and the neighboring prediction units so as to obtain a potential candidate subset and then conditionally selects the optimal mode through early likelihood decision and hybrid cost ranking. By the experiment-driven methodology, the proposed scheme implements the early termination if the best mode from the candidate subset is equal to one or two neighboring intramodes. The experimental results demonstrate that the proposed scheme averagely provides about 23.7% encoding speedup with just 0.82% BD-rate loss in comparison with default fast intramode decision in HM16.0. Compared to other fast intramode decision schemes, the proposed scheme also significantly reduces intracoding time while maintaining similar R-D performance for the all-intraconfiguration in HM16.0 Main profile.

scholarly journals Overview of HEVC extensions on screen content coding

2015 ◽  
Vol 4 ◽  
Author(s):  
Shan Liu ◽  
Xiaozhong Xu ◽  
Shawmin Lei ◽  
Kevin Jou
Keyword(s):  
Video Coding ◽  
High Efficiency ◽  
Expert Group ◽  
High Efficiency Video Coding ◽  
Screen Content ◽  
Draft Standard ◽  
Adaptive Motion ◽  
Screen Sharing ◽  
Screen Content Coding ◽  
Motion Picture Expert Group

Technologies for coding non-camera-captured video contents have received great interests lately due to the rapid growth of application areas such as wireless display and screen sharing, etc. In response to the market demands, the ITU-T Video Coding Expert Group and ISO/IEC Motion Picture Expert Group have jointly launched a new standardization project, i.e. the High-Efficiency Video Coding (HEVC) extensions on screen content coding (HEVC SCC). Several new video coding tools, including intra block copy, palette coding, adaptive color transform, and adaptive motion resolution, have been developed and adopted into HEVC SCC draft standard. This paper reviews the main features and coding technologies in the current HEVC SCC draft standard, with discussions about the performance and complexity aspects compared with prior arts.

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.

scholarly journals Time Reduction on 3D-HEVC Depth Maps Coding using Static Decision Trees Built Through Data Mining

2019 ◽  
Author(s):  
Mário Saldanha ◽  
Marcelo Porto ◽  
César Marcon ◽  
Luciano Agostini
Keyword(s):  
Data Mining ◽  
Decision Trees ◽  
Processing Time ◽  
High Efficiency ◽  
Rate Distortion ◽  
Depth Map ◽  
High Efficiency Video Coding ◽  
Block Partitioning ◽  
Coding Efficiency ◽  
Depth Maps

This dissertation presents a fast depth map coding for 3D-High Efficiency Video Coding (3D-HEVC) based on static Coding Unit (CU) splitting decision trees. The proposed solution is based on our previous works and avoids the costly Rate-Distortion Optimization (RDO) process for depth maps coding, which evaluates several possibilities of block partitioning and encoding modes for choosing the best one. This coding approach uses data mining and machine learning to extract the correlation among the encoder context attributes and to build the static decision trees. Each decision tree defines if a depth map CU must be split into smaller blocks, considering the encoding context through the evaluation of the CU features and encoder attributes. The results demonstrated that this approach can halve the 3D-HEVC encoder processing time with negligible coding efficiency loss. Besides, the obtained results surpass all related works regarding processing time and coding efficiency. The results reported in this dissertation were published in three journals and two events, besides generate a patent deposit. These products have the master student as the first author.

scholarly journals Block Based Enhancement using Deep Learning for Conversion of Low Resolution AVS Video to High Resolution HEVC Video

2021 ◽  
Author(s):  
Rizwan Qureshi ◽  
Mehmood Nawaz
Keyword(s):  
Deep Learning ◽  
High Efficiency ◽  
Motion Vector ◽  
Super Resolution ◽  
Computational Time ◽  
Video Frame ◽  
Low Resolution ◽  
High Efficiency Video Coding ◽  
High Definition ◽  
Resolution Method

Conversion of one video bitstream to another video bitstream is a challenging task in the heterogeneous transcoder due to different video formats. In this paper, a region of interest (ROI) based super resolution technique is used to convert the lowresolution AVS (audio video standard) video to high definition HEVC (high efficiency video coding) video. Firstly, we classify a low-resolution video frame into small blocks by using visual characteristics, transform coefficients, and motion vector (MV) of a video. These blocks are further classified as blocks of most interest (BOMI), blocks of less interest (BOLI) and blocks of noninterest (BONI). The BONI blocks are considered as background blocks due to less interest in video and remains unchanged during SR process. Secondly, we apply deep learning based super resolution method on low resolution BOMI, and BOLI blocks to enhance the visual quality. The BOMI and BOLI blocks have high attention due to ROI that include some motion and contrast of the objects. The proposed method saves 20% to 30% computational time and obtained appreciable results as compared with full frame based super resolution method. We have tested our method on different official video sequences with resolution of 1K, 2K, and 4K. Our proposed method has an efficient visual performance in contrast to the full frame-based super resolution method.

scholarly journals Rendering Distortion Estimation Model for 3D High Efficiency Depth Coding

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Qiuwen Zhang ◽  
Liang Tian ◽  
Lixun Huang ◽  
Xiaobing Wang ◽  
Haodong Zhu
Keyword(s):  
High Efficiency ◽  
Three Dimensional ◽  
Rate Distortion ◽  
Depth Map ◽  
Depth Image ◽  
High Efficiency Video Coding ◽  
Estimation Model ◽  
Depth Image Based Rendering ◽  
Distortion Estimation ◽  
3D Scene

A depth map represents three-dimensional (3D) scene geometry information and is used for depth image based rendering (DIBR) to synthesize arbitrary virtual views. Since the depth map is only used to synthesize virtual views and is not displayed directly, the depth map needs to be compressed in a certain way that can minimize distortions in the rendered views. In this paper, a modified distortion estimation model is proposed based on view rendering distortion instead of depth map distortion itself and can be applied to the high efficiency video coding (HEVC) rate distortion cost function process for rendering view quality optimization. Experimental results on various 3D video sequences show that the proposed algorithm provides about 31% BD-rate savings in comparison with HEVC simulcast and 1.3 dB BD-PSNR coding gain for the rendered view.

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