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International Conference on Wireless Communications and Signal processing (WCSP)

作者： Kaixiang Lin Dong Liu 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)9781479973408

How to make dynamic recommendations under volatile user interest drifts has been a problem of great interest in modern recommender systems, where challenges lie in accurate and efficient measurement, modeling, and prediction of the user interest drifts. This paper studies a category-based approach to the problem with the key idea that items are aggregated into categories and recommendations are made on each category. In our approach, we use the category-wise rating matrix to measure the changing preferences of users; we design a dynamic adaptive model (DAM) to describe the patterns of interest drifts; and we utilize linear regression to predict the future interests of users in a category-based manner. We have built a category-based dynamic recommender system and tested it with two well-known datasets. Experimental results show that our proposed approach achieves superior performance on category-based rating prediction compared with state-of-the-art dynamic recommendation algorithms.

关键词： Vectors Adaptation models Clustering algorithms Recommender systems Optimization Predictive models Data models

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Perceptual Evaluation of Pre-processing for Video Transcoding

Perceptual Evaluation of Pre-processing for Video Transcodin...

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IEEE Visual Communications and Image processing (VCIP)

作者： Shiyu Huang Ziyuan Luo Jiahua Xu Wei Zhou 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, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to construct the first Pre-processed and Transcoded Video Database (PTVD). Then, we conduct the subjective experiment, showing that compared with the video sent to the codec directly at the same bitrate, the appropriate pre-processing methods indeed improve the perceptual quality. Finally, existing image/video quality metrics are evaluated on our database. The results indicate that the performance of the existing image/video quality assessment (IQA/VQA) approaches remain to be improved. We will make our database publicly available soon.

关键词： Measurement Codecs Databases Visual communication Image processing Buildings Bit rate

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Light Field Compression Based on Implicit Neural Representation

Light Field Compression Based on Implicit Neural Representat...

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Picture Coding Symposium, PCS

作者： Henan Wang Hanxin Zhu 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)9781665492584

Light field, as a new data representation format in multimedia, has the ability to capture both intensity and direction of light rays. However, the additional angular information also brings a large volume of data. Classical coding methods are not effective to describe the relationship between different views, leading to redundancy left. To address this problem, we propose a novel light field compression scheme based on implicit neural representation to reduce redundancies between views. We store the information of a light field image implicitly in an neural network and adopt model compression methods to further compress the implicit representation. Extensive experiments have demonstrated the effectiveness of our proposed method, which achieves comparable rate-distortion performance as well as superior perceptual quality over traditional methods.

关键词： Image coding Redundancy Pipelines Neural networks Rate-distortion Light fields Encoding

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Light Field Compression Based on Implicit Neural Representation

arXiv

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arXiv 2024年

作者： Wang, Henan Zhu, Hanxin Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

关键词： Redundancy

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Reinforced Bit Allocation under Task-Driven Semantic Distortion Metrics

Reinforced Bit Allocation under Task-Driven Semantic Distort...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Jun Shi 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: (数字)9781728133201

ISBN: (纸本)9781728133218

Rapid growing intelligent applications require optimized bit allocation in image/video coding to support specific task-driven scenarios such as detection, classification, segmentation, etc. Some learning-based frameworks have been proposed for this purpose due to their inherent end-to-end optimization mechanisms. However, it is still quite challenging to integrate these task-driven metrics seamlessly into traditional hybrid coding framework. To the best of our knowledge, this paper is the first work trying to solve this challenge based on reinforcement learning (RL) approach. Specifically, we formulate the bit allocation problem as a Markovian Decision Process (MDP) and train RL agents to automatically decide the quantization parameter (QP) of each coding tree unit (CTU) for HEVC intra coding, according to the task-driven semantic distortion metrics. This bit allocation scheme can maximize the semantic level fidelity of the task, such as classification accuracy, while minimizing the bit-rate. We also employ gradient class activation map (Grad-CAM) and Mask R-CNN tools to extract task-related importance maps to help the agents make decisions. Extensive experimental results demonstrate the superior performance of our approach by achieving 43.1% to 73.2% bit-rate saving over the anchor of HEVC under the equivalent task-related distortions.

关键词： Task analysis Distortion Bit rate Image coding Semantics Encoding

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Visualization of user interests in online music services

Visualization of user interests in online music services

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Jingxian Zhang Dong Liu 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)9781479947164

Online music services have been popular for end users to obtain music, where user interests, as reflected by their downloading records, are crucial for service providers to understand users and thus to provide personalization. However, the raw downloading records are of huge volume and difficult to analyze intuitively. We study a visualization approach to analyzing downloading records so as to present user interests. To reveal the underlying relevance between music tracks, we utilized not only the metadata of music (especially genres), but also collaborative relevance that is voted by users. To present time varying user interests, we designed several new figures, namely Bean plot, Instrument plot, and Transitional Pie plot, that are capable in displaying different aspects of user interests variation. We have performed experiments with a real-world data set, and the results show the effectiveness of our proposed visualization method. Our work is also inspiring for visualization of time varying data in other applications.

关键词： Instruments Collaboration Color Statistical distributions Data visualization Neck Educational institutions

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3D-HEVC visual quality assessment: Database and bitstream model

3D-HEVC visual quality assessment: Database and bitstream mo...

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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Wei Zhou Ning Liao Zhibo Chen Weiping Li 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)9781509003556

Visual Quality Assessment of 3D/stereoscopic video (3D VQA) is significant for both quality monitoring and optimization of the existing 3D video services. In this paper, we build a 3D video database based on the latest 3D-HEVC video coding standard, to investigate the relationship among video quality, depth quality, and overall quality of experience (QoE) of 3D/stereoscopic video. We also analyze the pivotal factors to the video and depth qualities. Moreover, we develop a No-Reference 3D-HEVC bitstream-level objective video quality assessment model, which utilizes the key features extracted from the 3D video bitstreams to assess the perceived quality of the stereoscopic video. The model is verified to be effective on our database as compared with widely used 2D Full-Reference quality metrics as well as a state-of-the-art 3D FR pixel-level video quality metric.

关键词： Quality assessment Three-dimensional displays Video recording Databases Stereo image processing Visualization Cameras

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Deep Multi-Scale Features Learning for Distorted Image Quality Assessment

Deep Multi-Scale Features Learning for Distorted Image Quali...

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IEEE International Symposium on Circuits and systems

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

Image quality assessment (IQA) aims to estimate human perception based image visual quality. Although existing deep neural networks (DNNs) have shown significant effectiveness for tackling the IQA problem, it still needs to improve the DNN-based quality assessment models by exploiting efficient multi-scale features. In this paper, motivated by the human visual system (HVS) combining multi-scale features for perception, we propose to use pyramid features learning to build a DNN with hierarchical multi-scale features for distorted image quality prediction. Our model is based on both residual maps and distorted images in luminance domain, where the proposed network contains spatial pyramid pooling and feature pyramid from the network structure. Our proposed network is optimized in a deep end-to-end supervision manner. To validate the effectiveness of the proposed method, extensive experiments are conducted on four widely-used image quality assessment databases, demonstrating the superiority of our algorithm.

关键词： Image quality Visualization Databases Visual systems Streaming media Quality assessment Video recording

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Universal enhancement of salient object detection

Universal enhancement of salient object detection

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Runchun Ye 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)9781509060689

Recently, numerous salient object detection methods are proposed for different data types. And a reliable method, which can accurately extract complete salient objects, is beneficial to various vision tasks. However, existing methods may fail in highlighting the entire salient object uniformly. In this work, we propose a simple and universal framework aiming to improve the detection result of existing methods. To remove inaccurate salient regions, we apply location prior and adaptive de-noising to prior saliency maps extracted from existing methods in the pre-processing step. Then, an iteration optimization algorithm considering local smoothness and global similarity is introduced to refine the pre-processed saliency map. The experimental results show that the proposed framework can universally enhance the performance of state-of-the-art salient object detection methods for 2D, 3D and light field data.

关键词： Object detection Three-dimensional displays Optimization Two dimensional displays Color Image segmentation Computational modeling

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Deep Local and Global Spatiotemporal Feature Aggregation for Blind Video Quality Assessment

Deep Local and Global Spatiotemporal Feature Aggregation for...

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IEEE Visual Communications and Image processing (VCIP)

作者： Wei Zhou 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: (数字)9781728180687

ISBN: (纸本)9781728180694

In recent years, deep learning has achieved promising success for multimedia quality assessment, especially for image quality assessment (IQA). However, since there exist more complex temporal characteristics in videos, very little work has been done on video quality assessment (VQA) by exploiting powerful deep convolutional neural networks (DCNNs). In this paper, we propose an efficient VQA method named Deep SpatioTemporal video Quality assessor (DeepSTQ) to predict the perceptual quality of various distorted videos in a no-reference manner. In the proposed DeepSTQ, we first extract local and global spatiotemporal features by pre-trained deep learning models without fine-tuning or training from scratch. The composited features consider distorted video frames as well as frame difference maps from both global and local views. Then, the feature aggregation is conducted by the regression model to predict the perceptual video quality. Finally, experimental results demonstrate that our proposed DeepSTQ outperforms state-of-the-art quality assessment algorithms.

关键词： Quality assessment Video recording Feature extraction Spatiotemporal phenomena Databases Indexes Deep learning

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