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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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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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M-LVC: Multiple frames prediction for learned video compression

arXiv

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arXiv 2020年

作者： Lin, Jianping Liu, Dong Li, Houqiang Wu, Feng Cas Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027

We propose an end-to-end learned video compression scheme for low-latency scenarios. Previous methods are limited in using the previous one frame as reference. Our method introduces the usage of the previous multiple frames as references. In our scheme, the motion vector (MV) field is calculated between the current frame and the previous one. With multiple reference frames and associated multiple MV fields, our designed network can generate more accurate prediction of the current frame, yielding less residual. Multiple reference frames also help generate MV prediction, which reduces the coding cost of MV field. We use two deep auto-encoders to compress the residual and the MV, respec-tively. To compensate for the compression error of the autoencoders, we further design a MV refinement network and a residual refinement network, taking use of the multiple ref-erence frames as well. All the modules in our scheme are jointly optimized through a single rate-distortion loss func-tion. We use a step-by-step training strategy to optimize the entire scheme. Experimental results show that the proposed method outperforms the existing learned video compression methods for low-latency mode. Our method also performs better than H.265 in both PSNR and MS-SSIM. Our code and models are publicly available. Copyright © 2020, The Authors. All rights reserved.

关键词： Image compression

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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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Deep Grammatical Multi-classifier for Continuous Sign Language Recognition

Deep Grammatical Multi-classifier for Continuous Sign Langua...

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Chengcheng Wei Wengang Zhou Junfu Pu Houqiang Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

In this paper, we propose a novel deep architecture with multiple classifiers for continuous sign language recognition. Representing the sign video with a 3D convolutional residual network and a bidirectional LSTM, we formulate continuous sign language recognition as a grammatical-rule-based classification problem. We first split a text sentence of sign language into isolated words and n-grams, where an n-gram is a sequence of consecutive n words in a sentence. Then, we propose a word-independent classifiers (WIC) module and an n-gram classifier (NGC) module to identify the words and n-grams in a sentence, respectively. A greedy decoding algorithm is employed to integrate words and n-grams into the sentence based on the confidence scores provided by both modules. Our method is evaluated on a Chinese continuous sign language recognition benchmark, and the experimental results demonstrate its effectiveness and superiority.

关键词： Videos Assistive technology Gesture recognition Feature extraction Task analysis Decoding Cats

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Saliency & structure preserving multi-operator image retargeting

Saliency & structure preserving multi-operator image retarge...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Lingling Zhu Zhibo Chen Xiaoming Chen Ning Liao University of Science and Technology of China CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System Hefei China

ISBN: (纸本)9781479999897

Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can preserve important image contents and structure well without introducing deformation. To address this problem, we propose a Saliency & Structure Preserving Multi-operator (SSPM) method. SSPM classifies images into three categories utilizing SIFT density to improve performance of saliency preservation, helping to mitigate negative influence from center-bias property of most existing saliency detection models. SSPM also employs different principles to improve structure preservation performance, including Earth Mover's Distance (EMD) and Gray-Level Cooccurrence Matrix (GLCM) to get optimal operator sequences for smart content-aware image retargeting. SSPM method not only can well preserve salient contents and structure, but also can greatly improve deformation resilience. Experimental results demonstrated that our method outperforms state-of-art image retargeting methods.

关键词： Distortion Feature extraction Resilience Earth Loss measurement Visualization Visual perception

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Learning based Facial Image Compression with Semantic Fidelity Metric

arXiv

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arXiv 2018年

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

Surveillance and security scenarios usually require high efficient facial image compression scheme for face recognition and identification. While either traditional general image codecs or special facial image compression schemes only heuristically refine codec separately according to face verification accuracy metric. We propose a Learning based Facial Image Compression (LFIC) framework with a novel Regionally Adaptive Pooling (RAP) module whose parameters can be automatically optimized according to gradient feedback from an integrated hybrid semantic fidelity metric, including a successfully exploration to apply Generative Adversarial Network (GAN) as metric directly in image compression scheme. The experimental results verify the framework’s efficiency by demonstrating performance improvement of 71.41%, 48.28% and 52.67% bitrate saving separately over JPEG2000, WebP and neural network-based codecs under the same face verification accuracy distortion metric. We also evaluate LFIC’s superior performance gain compared with latest specific facial image codecs. Visual experiments also show some interesting insight on how LFIC can automatically capture the information in critical areas based on semantic distortion metrics for optimized compression, which is quite different from the heuristic way of optimization in traditional image compression algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： Image compression

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Fast genetic multi-operator image retargeting

Fast genetic multi-operator image retargeting

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IEEE Visual Communications and Image processing (VCIP)

作者： Lingling 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)9781509053179

Content-aware image retargeting has attracted substantial research interests in the related research community. However, so far there is still no method can adequately preserve important image contents and structure well without introducing conspicuous visible deformation in a relatively short period of time. To address this problem, we propose a Fast Genetic Multi-operator (FGM) method which integrates multiple retargeting operators. To improve the efficiency, FGM method utilizes Genetic Algorithms (GAs) to reach the optimal operator ratio, which adopts saliency and Gray-Level Co-occurrence Matrix (GLCM) as its energy function. FGM method not only can well preserve salient contents and structure, but also can greatly reduce the computational complexity. Experimental results demonstrated that our method outperforms state-of-art image retargeting methods.

关键词： Genetic algorithms Biological cells Genetics Distortion Computational complexity Visualization

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Padding-Aware Learned Image Compression

Padding-Aware Learned Image Compression

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Haotian Zhang Junqi Liao Yiheng Jiang Li Li Dong Liu CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

For current learned image compression methods, padding input images is necessary to meet the resolution requirements of down-sampling layers. However, the impact of padding has not been studied thoroughly. Most previous studies ignore padded images in the training process. In this paper, we analyze the impact of padding on compression performance. Then, we propose a padding-aware training (PAT) strategy, handling the padding effect during the training. Specifically, our PAT strategy calculates the loss of pre-padding image through a masking operation. Finally, according to our systematic experimental results, we find that images with different resolutions tend to favor different padding modes. Therefore, we further propose to conduct padding mode decision in the encoding process for rate-distortion optimization. Experiments demonstrate that our proposed PAT strategy and padding mode decision effectively compensate for the performance drop caused by padding.

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Edge-Guided Panoramic Video Stitching with Limited Overlap

Edge-Guided Panoramic Video Stitching with Limited Overlap

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Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Chaoyu Xie Xuejin 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)9781728123455

ISBN: (纸本)9781728123462

Video stitching remains a challenging problem in computer vision. In this paper, we propose a novel edge-guided method to stitch multiple videos that have small overlapped regions. Our algorithm consists of three steps: (1) spherical projection of the input video frames based on camera calibration, (2) edge detection and edge-guided feature matching for video registration, and (3) seam optimization to eliminate distortions and ghosts in the composited panoramic videos. The experimental results and user studies demonstrate that our method is robust to videos that have small overlapped regions and produces more visually pleasing panoramic videos than state-of-the-art techniques.

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