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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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Deeply Exploit Depth information for Object Detection

Deeply Exploit Depth Information for Object Detection

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Saihui Hou Zilei Wang Feng Wu CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

This paper addresses the issue on how to more effectively coordinate the depth with RGB aiming at boosting the performance of RGB-D object detection. Particularly, we investigate two primary ideas under the CNN model: property derivation and property fusion. Firstly, we propose that the depth can be utilized not only as a type of extra information besides RGB but also to derive more visual properties for comprehensively describing the objects of interest. So a two-stage learning framework consisting of property derivation and fusion is constructed. Here the properties can be derived either from the provided color/depth or their pairs (e.g. the geometry contour adopted in this paper). Secondly, we explore the fusion method of different properties in feature learning, which is boiled down to, under the CNN model, from which layer the properties should be fused together. The analysis shows that different semantic properties should be learned separately and combined before passing into the final classifier. Actually, such a detection way is in accordance with the mechanism of the primary neural cortex (V1) in brain. We experimentally evaluate the proposed method on the challenging dataset, and have achieved state-of-the-art performance.

关键词： Object detection Visualization Image color analysis Feature extraction geometry Gravity Computer vision

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Image annotation via social diffusion analysis with common interests

Image annotation via social diffusion analysis with common i...

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IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Chenyi Lei Dong Liu CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

Automatic annotation of images is of crucial importance in image retrieval and management systems. Most of the existing annotation methods rely on content-based approach to annotation, whose effectiveness is restricted due to the semantic gap between low-level features and semantic annotations, as well as the irrelevance between annotations and image content. Recently, social media analysis has been investigated for image annotation. Inspired by the abundant social diffusion records of images in online social networks, we propose a novel image annotation approach based on social diffusion analysis. We present a common-interest model to interpret social diffusion, i.e. different images have different social diffusion routes due to the preferences of users, and such preferences are represented as common interests of pairwise users rather than personalized interests. We propose an image annotation framework that consists of learning of common interests, feature extraction from social diffusion records, and automatic annotation by learning to rank. Experimental results on a real-world dataset show that our proposed approach outperforms content-based and user-preference-based annotation methods.

关键词： Abstracts Vectors Positron emission tomography

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Combining directional intra prediction and intra block copy with block partition for HEVC

Combining directional intra prediction and intra block copy ...

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IEEE International Conference on Image processing

作者： Yue Li Li Li Dong Liu Houqiang Li Feng Wu CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China

The directional intra prediction (DIP) modes in HEVC are capable of predicting local continuous image features. Recently, intra block copy (IBC) is proposed for screen content coding, aiming at predicting non-local recurrent image features. For natural video, we observe that recurrent features are often irregular and not aligned with blocks. Thus, we propose a combination of DIP and IBC with block partition for better intra prediction, where one block can be divided into several partitions, each of which may choose between DIP and IBC. We study an intra prediction scheme with the proposed combination, especially the rate-distortion optimization and entropy coding in the scheme. Preliminary experimental results show that the proposed combined intra prediction achieves as high as 5.8% bit-rate saving compared to HEVC anchor.

关键词： Electronics packaging Rate-distortion Optimization Entropy coding Motion estimation Complexity theory

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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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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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Hierarchical quadtree-based flexible block ordering in HEVC intra coding

Hierarchical quadtree-based flexible block ordering in HEVC ...

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IEEE Visual Communications and Image processing (VCIP)

作者： Lei Guo Dong Liu Li Li 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)9781509053179

In all of the existing block-based image and video coding standards, blocks are processed in the fixed scan order. Then in HEVC intra coding, intra prediction is always based on the top and/or left neighboring reconstructed pixels, which incurs less accurate prediction for blocks where the spatial correlation is not along the topleft-to-bottomright direction. To obtain better intra prediction, we propose to flexibly determine the coding order of blocks in HEVC intra coding. Complying with the hierarchical quadtree structure in HEVC, our flexible block ordering (FBO) technique recursively decides the coding order of four sub-blocks when splitting one block. Moreover, we propose new methods to perform inter/extrapolation for intra prediction so as to fully utilize neighboring reconstructed pixels, not always being top/left. Experimental results show that our proposed FBO technique achieves on average 2.9% BD-rate reduction compared to HEVC baseline.

关键词： Encoding High efficiency video coding Image reconstruction Correlation Standards Interpolation Copper

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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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Tensor oriented no-reference light field image quality assessment

arXiv

引用

arXiv 2019年

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

Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media. However, due to the inherent high dimensional characteristics of LFI, the LFI quality assessment turns into a multi-dimensional problem that requires consideration of the quality degradation in both spatial and angular dimensions. Therefore, we propose a novel Tensor oriented No-reference Light Field image Quality evaluator (Tensor-NLFQ) based on tensor theory. Specifically, since the LFI is regarded as a low-rank 4D tensor, the principle components of four oriented sub-aperture view stacks are obtained via Tucker decomposition. Then, the Principal Component spatial Characteristic (PCSC) is designed to measure the spatial-dimensional quality of LFI considering its global naturalness and local frequency properties. Finally, the Tensor Angular Variation Index (TAVI) is proposed to measure angular consistency quality by analyzing the structural similarity distribution between the first principal component and each view in the view stack. Extensive experimental results on four publicly available LFI quality databases demonstrate that the proposed Tensor-NLFQ model outperforms state-of-the-art 2D, 3D, multi-view, and LFI quality assessment algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Tensors

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Generative Adversarial Network-Based Frame Extrapolation for Video Coding

Generative Adversarial Network-Based Frame Extrapolation for...

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IEEE Visual Communications and Image processing (VCIP)

作者： Jianping Lin Dong Liu Houqiang Li 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

Motion estimation and motion compensation are fundamental in video coding to remove the temporal redundancy between video frames. The current video coding schemes usually adopt block-based motion estimation and compensation using simple translational or affine motion models, which cannot efficiently characterize complex motions in natural video signal. In this paper, we propose a frame extrapolation method for motion estimation and compensation. Specifically, based on the several previous frames, our method directly extrapolates the current frame using a trained deep network model. The deep network we adopted is a redesigned Video Coding oriented LAplacian Pyramid of Generative Adversarial Networks (VC-LAPGAN). The extrapolated frame is then used as an additional reference frame. Experimental results show that the VC-LAPGAN is capable in estimating and compensating for complex motions, and extrapolating frames with high visual quality. Using the VC-LAPGAN, our method achieves on average 2.0% BD-rate reduction than High Efficiency Video Coding (HEVC) under low-delay P configuration.

关键词： Video coding Motion estimation Training Computational modeling Extrapolation Laplace equations Convolutional codes

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