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Quality assessment of stereoscopic 360-degree images from multi-viewports

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arXiv 2019年

作者： Xu, Jiahua Luo, Ziyuan Zhou, Wei Zhang, Wenyuan Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 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. Copyright © 2019, The Authors. All rights reserved.

关键词： Quality of service

Learning for video compression

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arXiv 2018年

作者： Chen, Zhibo He, Tianyu Jin, Xin Wu, Feng CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

One key challenge to learning-based video compression is that motion predictive coding, a very effective tool for video compression, can hardly be trained into a neural network. In this paper we propose the concept of PixelMotionCNN (PMCNN) which includes motion extension and hybrid prediction networks. PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network. On the basis of PMCNN, we further explore a learning-based framework for video compression with additional components of iterative analysis/synthesis, binarization, etc. Experimental results demonstrate the effectiveness of the proposed scheme. Although entropy coding and complex configurations are not employed in this paper, we still demonstrate superior performance compared with MPEG-2 and achieve comparable results with H.264 codec. The proposed learning-based scheme provides a possible new direction to further improve compression efficiency and functionalities of future video coding. Copyright © 2018, The Authors. All rights reserved.

关键词： Image compression

Two-stream action recognition-oriented video super-resolution

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arXiv 2019年

作者： Zhang, Haochen Liu, Dong Xiong, Zhiwei CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

We study the video super-resolution (SR) problem for facilitating video analytics tasks, e.g. action recognition, instead of for visual quality. The popular action recognition methods based on convolutional networks, exemplified by two-stream networks, are not directly applicable on video of low spatial resolution. This can be remedied by performing video SR prior to recognition, which motivates us to improve the SR procedure for recognition accuracy. Tailored for two-stream action recognition networks, we propose two video SR methods for the spatial and temporal streams respectively. On the one hand, we observe that regions with action are more important to recognition, and we propose an optical-flow guided weighted mean-squared-error loss for our spatial-oriented SR (SoSR) network to emphasize the reconstruction of moving objects. On the other hand, we observe that existing video SR methods incur temporal discontinuity between frames, which also worsens the recognition accuracy, and we propose a siamese network for our temporal-oriented SR (ToSR) training that emphasizes the temporal continuity between consecutive frames. We perform experiments using two state-of-the-art action recognition networks and two well-known datasets–UCF101 and HMDB51. Results demonstrate the effectiveness of our proposed SoSR and ToSR in improving recognition accuracy. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

A Bayesian adaptive weighted total generalized variation model for image restoration

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A Bayesian adaptive weighted total generalized variation mod...

IEEE International Conference on Image processing

作者： Zhenbo Lu Houqiang Li Weiping Li CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Heifei Anhui China

In recent years, the Total Generalized Variation (TGV) model has received lots of attention in image processing community. Though this model can restore image with natural intensity transitions, its spatial identical parameter setting limits its performance. In this paper, we propose a novel Adaptive Weighted Total Generalized Variation model for image restoration. We analyze the TGV model from Bayesian Probability view and derive a novel adaptive parameter calculation scheme for it, exploiting the image's self-similarity. Experiment results on image deblurring and reconstruction show that by adapting the parameters in TGV model to image contents, the proposed model can restore image's edges and details well and achieve significant improvement over state of the art variational based models.

关键词： Adaptation models Image restoration Mathematical model Approximation methods Bayes methods TV Minimization

Efficient structure-preserving superpixel segmentation based on minimum spanning tree

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Efficient structure-preserving superpixel segmentation based...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Yu Bai Xuejin Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China

ISBN: (纸本)9781467372596

We propose a novel superpixel algorithm based on Minimum Spanning Tree (MST), to generate superpixels efficiently while strictly adhere to object boundaries. The MST, which built by gradually removing strong edges of the image graph extracted from the image, is more sensitive to image local structures. Therefore, an efficient hierarchical clustering strategy is basically employed in our algorithm to segment the input image into superpixels based on the tree distance. To gradually merge the image pixels and remove texture noises, a multi-layer scheme with different resolutions of superpixels is proposed. In each layer, the graph is constructed from the lower layer and segmented into superpixels in a linear complexity with the node number in the graph. Because the node number in each layer is exponentially reduced, the computational time of our method mainly concentrates on the first few layers, which is linear with the number of image pixels. The experimental results conducted on the Berkeley Segmentation Dataset demonstrate that our method outperforms state-of-the-art methods both in terms of structure preservation and computational efficiency.

关键词： Image segmentation Image color analysis Image edge detection Clustering algorithms Partitioning algorithms Joining processes Colored noise

Hierarchical co-segmentation of 3D point clouds for indoor scene

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Hierarchical co-segmentation of 3D point clouds for indoor s...

International Conference on systems, Signals and Image processing, IWSSIP

作者： Yanting Lin CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei City China

ISBN: (纸本)9781509063451

Segmentation of point clouds has been studied under a variety of scenarios. However, the segmentation of scanned point clouds for a clustered indoor scene remains significantly challenging due to noisy and incomplete data, as well as scene complexity. Based on the observation that objects in an indoor scene vary largely in scale but are typically supported by planes, we propose a co-segmentation approach. This technique utilizes the mutual agency between the point clouds captured at different times after the objects' poses change due to human actions. Hence, we hierarchically segment scenes from different times into patches and generate tree structures to store their relations. By iteratively clustering patches and co-analyzing them based on the relations between patches, we modify the tree structures and generate our results. To test the robustness of our method, we evaluate it on imperfectly scanned point clouds from a childroom, a bedroom, and two offices scenes.

关键词： Three-dimensional displays Image color analysis geometry Mathematical model Principal component analysis Silicon

VNVC: A Versatile Neural Video Coding Framework for Efficient Human-Machine Vision

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arXiv 2023年

作者： Sheng, Xihua Li, Li Liu, Dong Li, Houqiang The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Almost all digital videos are coded into compact representations before being transmitted. Such compact representations need to be decoded back to pixels before being displayed to humans and – as usual – before being enhanced/analyzed by machine vision algorithms. Intuitively, it is more efficient to enhance/analyze the coded representations directly without decoding them into pixels. Therefore, we propose a versatile neural video coding (VNVC) framework, which targets learning compact representations to support both reconstruction and direct enhancement/analysis, thereby being versatile for both human and machine vision. Our VNVC framework has a feature-based compression loop. In the loop, one frame is encoded into compact representations and decoded to an intermediate feature that is obtained before performing reconstruction. The intermediate feature can be used as reference in motion compensation and motion estimation through feature-based temporal context mining and cross-domain motion encoder-decoder to compress the following frames. The intermediate feature is directly fed into video reconstruction, video enhancement, and video analysis networks to evaluate its effectiveness. The evaluation shows that our framework with the intermediate feature achieves high compression efficiency for video reconstruction and satisfactory task performances with lower complexities. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

No-reference light field image quality assessment based on spatial-angular measurement

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arXiv 2019年

作者： Shi, Likun Zhou, Wei Chen, Zhibo CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Light field image quality assessment (LFI-QA) is a significant and challenging research problem. It helps to better guide light field acquisition, processing and applications. However, only a few objective models have been proposed and none of them completely consider intrinsic factors affecting the LFI quality. In this paper, we propose a No-Reference Light Field image Quality Assessment (NR-LFQA) scheme, where the main idea is to quantify the LFI quality degradation through evaluating the spatial quality and angular consistency. We first measure the spatial quality deterioration by capturing the naturalness distribution of the light field cyclopean image array, which is formed when human observes the LFI. Then, as a transformed representation of LFI, the Epipolar Plane Image (EPI) contains the slopes of lines and involves the angular information. Therefore, EPI is utilized to extract the global and local features from LFI to measure angular consistency degradation. Specifically, the distribution of gradient direction map of EPI is proposed to measure the global angular consistency distortion in the LFI. We further propose the weighted local binary pattern to capture the characteristics of local angular consistency degradation. Extensive experimental results on four publicly available LFI quality datasets demonstrate that the proposed method outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Deterioration

Experiment research on scattering characters of passive calibrator for high resolution SAR calibration

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Experiment research on scattering characters of passive cali...

2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Ming, Feng Hong, Jun Zhang, Rui Key Laboratory of Spatial Information Processing and Application System Technology Chinese Academy of Sciences China Institute of Electronics Chinese Academy of Sciences Beijing China Graduate University of the Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424427314

In this paper, the scattering characters of passive calibrator are experimentally researched for SAR calibration applications. The works are focused on the SAR image of corner reflectors larger than the resolution cells. What is more, the RCS of the nonorthogonal and unevenness corner reflector are also studied. Finally, for full PolSAR calibration, we also discover some interesting phenomena for the first time, which are very important for the calibration activity. ©2009 IEEE.

关键词： Calibration