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A Rate Control Algorithm for Video-based Point Cloud Compression

A Rate Control Algorithm for Video-based Point Cloud Compres...

引用

IEEE visual communications and image processing (VCIP)

作者： Fangyu Shen Wei Gao School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9781728173221

Video-based point cloud compression (V-PCC) has been an emerging compression technology that projects the 3D point cloud into a 2D plane and uses high efficiency video coding (HEVC) to encode the projected 2D videos (geometry video and color video). In this work, we propose a rate control algorithm for the all-intra (AI) configuration of V-PCC. Specifically, based on the quality-dependency existing in the projected videos, we develop an optimization formulation to allocate target bits between the geometry video and the color video. Furthermore, we design a two-pass method for HEVC to adapt to the new characteristics of projected videos, which significantly improves the accuracy of rate control. Experimental results demonstrate that our algorithm outperforms V-PCC without rate control in R-D performance with just 0.43% bitrate error.

关键词： Point cloud compression Geometry Three-dimensional displays image color analysis visual communication Bit rate Artificial intelligence

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Linear Regression Mode of Intra Prediction for Screen Content Coding

Linear Regression Mode of Intra Prediction for Screen Conten...

引用

IEEE visual communications and image processing (VCIP)

作者： Wei Peng Hongkui Wang Li Yu School of Electron. Inf. & Commun. Huazhong Univ. of Sci. & Tech. Wuhan Hubei China

ISBN: (纸本)9781728173221

High Efficiency Video Coding - Screen Content Coding (HEVC-SCC) follows the traditional angular intra prediction technique in HEVC. However, the Planar mode and the DC mode are somewhat repetitive for screen content video with features such as no senor noise. Hence, this paper proposes a new intra prediction mode called linear regression (LR) mode, which combines the Planar mode and the DC mode into one mode. The LR mode improves the prediction accuracy of intra prediction for fading regions in screen content video. Besides, by optimizing the most probable mode (MPM) construction, the hit rate of the best mode in the MPM list is improved. The experimental results show that the proposed method can achieve 0.57% BD-BR reduction compared with HM $16.20+\text{SCM} 8.8$ , while the coding time remains largely the same.

关键词： Fading channels image coding Correlation visual communication Linear regression Bit rate Predictive models

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Attention-guided Convolutional Neural Network for Lightweight JPEG Compression Artifacts Removal

Attention-guided Convolutional Neural Network for Lightweigh...

引用

IEEE visual communications and image processing (VCIP)

作者： Gang Zhang Haoquan Wang Yedong Wang Haijie Shen School of Microelectronics Tianjin University Tianjin China Hisense Electric Co. Ltd. Multimedia R&D Center Qingdao China

ISBN: (纸本)9781728173221

JPEG compression artifacts seriously affect the viewing experience. While previous studies mainly focused on the deep convolutional networks for compression artifacts removal, of which the model size and inference speed limit their application prospects. In order to solve the above problems, this paper proposed two methods that can improve the training performance of the compact convolution network without slowing down its inference speed. Firstly, a fully explainable attention loss is designed to guide the network for training, which is calculated by local entropy to accurately locate compression artifacts. Secondly, Fully Expanded Block (FEB) is proposed to replace the convolutional layer in compact network, which can be contracted back to a normal convolutional layer after the training process is completed. Extensive experiments demonstrate that the proposed method outperforms the existing lightweight methods in terms of performance and inference speed.

关键词： Training Performance evaluation image coding visual communication Convolution Transform coding Video compression

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Entropy-based Deep Product Quantization for visual Search and Deep Feature Compression

Entropy-based Deep Product Quantization for Visual Search an...

引用

IEEE visual communications and image processing (VCIP)

作者： Benben Niu Ziwei Wei Yun He Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Beijing China

ISBN: (纸本)9781728173221

With the emergence of various machine-to-machine and machine-to-human tasks with deep learning, the amount of deep feature data is increasing. Deep product quantization is widely applied in deep feature retrieval tasks and has achieved good accuracy. However, it does not focus on the compression target primarily, and its output is a fixed-length quantization index, which is not suitable for subsequent compression. In this paper, we propose an entropy-based deep product quantization algorithm for deep feature compression. Firstly, it introduces entropy into hard and soft quantization strategies, which can adapt to the codebook optimization and codeword determination operations in the training and testing processes respectively. Secondly, the loss functions related to entropy are designed to adjust the distribution of quantization index, so that it can accommodate to the subsequent entropy coding module. Experimental results carried on retrieval tasks show that the proposed method can be generally combined with deep product quantization and its extended schemes, and can achieve a better compression performance under near lossless condition.

关键词： Training visualization Quantization (signal) image coding visual communication Entropy Entropy coding

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Stereo image Super-Resolution Based on Pixel-Wise Knowledge Distillation Strategy

Stereo Image Super-Resolution Based on Pixel-Wise Knowledge ...

引用

IEEE visual communications and image processing (VCIP)

作者： Li Ma Sumei Li School of electrical and information engineering Tianjin University Tianjin China

ISBN: (纸本)9781728173221

In stereo image super-resolution (SR), it is equally important to utilize intra-view and cross-view information. However, most existing methods only focus on the exploration of cross-view information and neglect the full mining of intra-view information, which limits the reconstruction performance of these methods. Since single image SR (SISR) methods are powerful in intra-view information exploitation, we propose to introduce the knowledge distillation strategy to transfer the knowledge of a SISR network (teacher network) to a stereo image SR network (student network). With the help of the teacher network, the student network can easily learn more intra-view information. Specifically, we propose pixel-wise distillation as the implementation method, which not only improves the intra-view information extraction ability of student network, but also ensures the effective learning of cross-view information. Moreover, we propose a lightweight student network named Adaptive Residual Feature Aggregation network (ARFAnet). Its main unit, the ARFA module, can aggregate informative residual features and produce more representative features for image reconstruction. Experimental results demonstrate that our teacher-student network achieves state-of-the-art performance on all benchmark datasets.

关键词： Knowledge engineering Adaptive systems visual communication Aggregates Superresolution Neural networks Benchmark testing

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Dictionary Learning-based Reference Picture Resampling in VVC

Dictionary Learning-based Reference Picture Resampling in VV...

引用

IEEE visual communications and image processing (VCIP)

作者： Jens Schneider Christian Rohlfing Institut für Nachrichtentechnik RWTH Aachen University

ISBN: (纸本)9781728173221

Versatile Video Coding (VVC) introduces the con-cept of Reference Picture Resampling (RPR), which allows for a resolution change of the video during decoding, without introducing an additional Intra Random Access Point (IRAP) into the bitstream. When the resolution is increased, an upsampling operation of the reference picture is required in order to apply motion compensated prediction. Conceptually, the upsampling by linear interpolation filters fails to recover frequencies which were lost during downsampling. Yet, the quality of the upsampled reference picture is crucial to the pre-diction performance. In recent years, machine learning based Super-Resolution (SR) has shown to outperform conventional interpolation filters by far in regard to super-resolving a previ-ously downsampled image. In particular, Dictionary Learning-based Super-Resolution (DLSR) was shown to improve the inter-layer prediction in SHVC [1]. Thus, this paper introduces DLSR to the prediction process in RPR. Further, the approach is experimentally evaluated by an implementation based on the VTM-9.3 reference software. The simulation results show a reduction of the instantaneous bitrate of 0.98% on average at the same objective quality in terms of PSNR. Moreover, the peak bitrate reduction is measured to 4.74% for the “Johnny” sequence of the JVET test set.

关键词： Video coding Interpolation image coding Dictionaries visual communication Simulation Bit rate

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360HRL: Hierarchical Reinforcement Learning Based Rate Adaptation for 360-Degree Video Streaming

360HRL: Hierarchical Reinforcement Learning Based Rate Adapt...

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IEEE visual communications and image processing (VCIP)

作者： Jun Fu Chen Hou Zhibo Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

Recently, reinforced adaptive bitrate (ABR) algorithms have achieved remarkable success in tile-based 360-degree video streaming. However, they heavily rely on accurate viewport prediction. To alleviate this issue, we propose a hierarchical reinforcement-learning (RL) based ABR algorithm, dubbed 360HRL. Specifically, 360HRL consists of a top agent and a bottom agent. The former is used to decide whether to download a new segment for continuous playback or re-download an old segment for correcting wrong bitrate decisions caused by inaccurate viewport estimation, and the latter is used to select bitrates for tiles in the chosen segment. In addition, 360HRL adopts a two-stage training methodology. In the first stage, the bottom agent is trained under the environment where the top agent always chooses to download a new segment. In the second stage, the bottom agent is fixed and the top agent is optimized with the help of a heuristic decision rule. Experimental results demonstrate that 360HRL outperforms existing RL-based ABR algorithms across a broad of network conditions and quality of experience (QoE) objectives.

关键词： Training visual communication image processing Bit rate Estimation Reinforcement learning Streaming media

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CNN-based Super Resolution for Video Coding Using Decoded Information

CNN-based Super Resolution for Video Coding Using Decoded In...

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IEEE visual communications and image processing (VCIP)

作者： Chaoyi Lin Yue Li Kai Zhang Zhaobin Zhang Li Zhang Multimedia Lab Bytedance Inc.

ISBN: (纸本)9781728173221

Down-sampling followed by an up-sampling is a well-known strategy to compress high-resolution pictures given a limited bandwidth in image as well as video coding. Recently, inspired by the latest advances of image super resolution (SR) technologies using convolutional neural network (CNN), CNN-based SR has been explored for resampling-based coding. However, the side information generated during the compression process is not utilized efficiently by the SR network in prior arts. In this paper, we propose a CNN-based SR method for video coding, where more side information is leveraged as a supplement to reconstruction samples. Specifically, we introduce prediction samples to be the auxiliary information, as it can provide the texture and directional information about the original picture. Considering the different characteristics, we design different networks for the luma and chroma components. When designing the chroma up-sampling CNN, the luma reconstruction is used as the auxiliary information of the chroma network, which exploits the cross-component correlation. Experimental results show that the proposed method achieves 11.07% BD-rate savings in all-intra configuration compared with VTM-11.0. Further experiments validate the effectiveness of using luma information to aid the chroma up-sampling process.

关键词： Video coding image coding image resolution Correlation visual communication Bit rate Bandwidth

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DCTResNet: Transform Domain image Deblocking for Motion Blur images

DCTResNet: Transform Domain Image Deblocking for Motion Blur...

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IEEE visual communications and image processing (VCIP)

作者： Paras Maharjan Ning Xu Xuan Xu Yuyan Song Zhu Li University of Missouri Kansas City USA Kwai Inc. CA USA

ISBN: (纸本)9781728173221

Pixel recovery with deep learning has shown to be very effective for a variety of low-level vision tasks like image super-resolution, denoising, and deblurring. Most existing works operate in the spatial domain, and there are few works that exploit the transform domain for image restoration tasks. In this paper, we present a transform domain approach for image deblocking using a deep neural network called DCTResNet. Our application is compressed video motion deblur, where the input video frame has blocking artifacts that make the deblurring task very challenging. Specifically, we use a block-wise Discrete Cosine Transform (DCT) to decompose the image into its low and high-frequency sub-band images and exploit the strong sub-band specific features for more effective deblocking solutions. Since JPEG also uses DCT for image compression, using DCT sub-band images for image deblocking helps to learn the JPEG compression prior to effectively correct the blocking artifacts. Our experimental results show that both PSNR and SSIM for DCTResNet perform more favorably than other state-of-the-art (SOTA) methods, while significantly faster in inference time.

关键词： Deep learning image coding Superresolution Noise reduction Transform coding Transforms image restoration

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Faster and Finer Pose Estimation for Object Pool in a Single RGB image

Faster and Finer Pose Estimation for Object Pool in a Single...

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IEEE visual communications and image processing (VCIP)

作者： Lee Aing Wen-Nung Lie Jui-Chiu Chiang National Chung Cheng University (CCU) Chiayi Taiwan Center for Innovative Research on Aging Society (CIRAS) National Chung Cheng University (CCU) Chiayi Taiwan Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI) National Chung Cheng University (CCU) Chiayi Taiwan

ISBN: (纸本)9781728173221

Predicting/estimating the 6DoF pose parameters for multi-instance objects accurately in a fast manner is an important issue in robotic and computer vision. Even though some bottom-up methods have been proposed to be able to estimate multiple instance poses simultaneously, their accuracy cannot be considered as good enough when compared to other state-of-the-art top-down methods. Their processing speed still cannot respond to practical applications. In this paper, we present a faster and finer bottom-up approach of deep convolutional neural network to estimate poses of the object pool even multiple instances of the same object category present high occlusion/overlapping. Several techniques such as prediction of semantic segmentation map, multiple keypoint vector field, and 3D coordinate map, and diagonal graph clustering are proposed and combined to achieve the purpose. Experimental results and ablation studies show that the proposed system can achieve comparable accuracy at a speed of 24.7 frames per second for up to 7 objects by evaluation on the well-known Occlusion LINEMOD dataset.

关键词： Computer vision Three-dimensional displays visual communication Robot kinematics image processing Semantics Pose estimation

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