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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 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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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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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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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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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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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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Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports

Quality Assessment of Stereoscopic 360-degree Images from Mu...

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Picture Coding Symposium, PCS

作者： Jiahua Xu Ziyuan Luo Wei Zhou Wenyuan Zhang Zhibo Chen CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

Objective quality assessment of stereoscopic panoramic images becomes a challenging problem owing to the rapid growth of 360-degree contents. Different from traditional 2D image quality assessment (IQA), more complex aspects are involved in 3D omnidirectional IQA, especially unlimited field of view (FoV) and extra depth perception, which brings difficulty to evaluate the quality of experience (QoE) of 3D omnidirectional images. In this paper, we propose a multi-viewport based full-reference stereo 360 IQA model. Due to the freely changeable viewports when browsing in the head-mounted display, our proposed approach processes the image inside FoV rather than the projected one such as equirectangular projection (ERP). In addition, since overall QoE depends on both image quality and depth perception, we utilize the features estimated by the difference map between left and right views which can reflect disparity. The depth perception features along with binocular image qualities are employed to further predict the overall QoE of 3D 360 images. The experimental results on our public Stereoscopic OmnidirectionaL Image quality assessment Database (SOLID) show that the proposed method achieves a significant improvement over some well-known IQA metrics and can accurately reflect the overall QoE of perceived images.

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Objective quality assessment for image retargeting based on hybrid distortion pooled model

Objective quality assessment for image retargeting based on ...

引用

International Workshop on Quality of Multimedia Experience, QoMEx

作者： Jianxin Lin Lingling Zhu Zhibo Chen Xiaoming 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)9781479989591

With the increasing popularity of mobile devices, there are more and more screens with heterogeneous resolutions. In order to solve the mismatching problem of images displaying on different screens, various image retargeting techniques have been proposed. However, little effective objective quality assessment metric for image retargeting has been proposed. In this paper, we propose an objective image retargeting quality assessment method based on Hybrid Distortion Pooled Model (HDPM) considering image local similarity, content information loss and image structural distortion. The proposed HDPM method measures the retargeted image's local similarity based on matching the similar block by Scale-Invariant Features Transform (SIFT) features and computing the corresponding blocks' similarity by structural similarity (SSIM). Furthermore, the image content information loss in retargeted image, which is regarded as the SIFT feature loss, is taken into account. Besides, we also consider image's structural distortion in the proposed method, which is based on GLCM (Gray-level co-occurrence matrix). To evaluate the effectiveness of the proposed method, extensive experiments have been conducted, and the results show improved consistency between the proposed HDPM method and the corresponding subjective evaluations.

关键词： Distortion Quality assessment Correlation Feature extraction Distortion measurement Image edge detection Image quality

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A CNN-Based In-Loop Filter with CU Classification for HEVC

A CNN-Based In-Loop Filter with CU Classification for HEVC

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IEEE Visual Communications and Image processing (VCIP)

作者： Yuanying Dai Dong Liu Zheng-Jun Zha Feng Wu 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)9781538644591;9781538644584

Lossy compression of image and video yields visually annoying artifacts including blocking, blurring, ringing, etc., especially at low bit rates. In-loop filtering techniques can reduce these artifacts, improve quality, and achieve coding gain accordingly. In this paper, we present a convolutional neural network (CNN) based in-loop filter for High Efficiency Video Coding (HEVC). First, we design a new CNN structure that is composed of multiple Variable-filter-size Residue-learning blocks, namely VRCNN-ext, for artifact reduction. VRCNN-ext is trained by natural images as well as their compressed versions at different quality levels. Second, we investigate a new in-loop filter based on the trained VRCNN-ext models. Specifically, we observed that using VRCNN-ext directly on the inter pictures is not effective. To solve this problem, we further train a classifier to decide whether to use VRCNN-ext for each coding unit (CU). The classifier makes decision based on the compressed information, thus avoiding the overhead bits to control the on/off of the CNN-based filter at the CU level. Experimental results show that our scheme achieves significant bits saving than the HEVC anchor, leading to on average 9.2%, 9.6% and 7.4% BD-rate reduction on the HEVC test sequences, under all-intra, low-delay B and random-access configurations, respectively.

关键词： Decoding Encoding Training Image coding Feature extraction Copper Video coding

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