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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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AutoDerain: Memory-efficient Neural Architecture Search for Image Deraining

AutoDerain: Memory-efficient Neural Architecture Search for ...

引用

IEEE Visual Communications and Image processing (VCIP)

作者： Jun Fu Chen Hou 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

Learning-based image deraining methods have achieved remarkable success in the past few decades. Currently, most deraining architectures are developed by human experts, which is a laborious and error-prone process. In this paper, we present a study on employing neural architecture search (NAS) to automatically design deraining architectures, dubbed AutoDerain. Specifically, we first propose an U-shaped deraining architecture, which mainly consists of residual squeeze-and-excitation blocks (RSEBs). Then, we define a search space, where we search for the convolutional types and the use of the squeeze-and-excitation block. Considering that the differentiable architecture search is memory-intensive, we propose a memory-efficient differentiable architecture search scheme (MDARTS). In light of the success of training binary neural networks, MDARTS optimizes architecture parameters through the proximal gradient, which only consumes the same GPU memory as training a single deraining model. Experimental results demonstrate that the architecture designed by MDARTS is superior to manually designed derainers.

关键词： Training Visual communication Image processing Memory architecture Neural networks Graphics processing units

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QUERY DIFFICULTY ESTIMATION VIA PSEUDO RELEVANCE FEEDBACK FOR IMAGE SEARCH

QUERY DIFFICULTY ESTIMATION VIA PSEUDO RELEVANCE FEEDBACK FO...

引用

IEEE International Conference on Multimedia and Expo

作者： Qianghuai Jia Xinmei Tian Tao Mei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Microsoft Research

ISBN: (纸本)9781479947607

Query difficulty estimation (QDE) attempts to automatically predict the performance of the search results returned for a given query. QDE has been widely investigated in text document retrieval for many years. However, few research works have been explored in image retrieval. State-of-the-art QDE methods in image retrieval mainly investigate the statistical characteristics (coherence, robustness, etc.) of the returned images to derive a value for indicating the query difficulty degree. To the best of our knowledge, little research has been done to directly estimate the real retrieval performance of the search results, such as average precision, instead of only an indicator. In this paper, we propose a novel query difficulty estimation approach which automatically estimate the average precision of the image search results. Specifically, we first select a set of query relevant and query irrelevant images for each query via pseudo relevance feedback. Then an efficient and effective voting scheme is proposed to estimate the relevance label of each image in the search results. Based on the images' relevance labels, the average precision of the search results returned for the given query is derived. The experimental results on a benchmark image search dataset demonstrate the effectiveness of the proposed method.

关键词： Query difficulty estimation (QDE) Image retrieval Average precision (AP) Pseudo relevance feedback (PRF) Voting scheme

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Semantically Scalable Image Coding With Compression of Feature Maps

Semantically Scalable Image Coding With Compression of Featu...

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IEEE International Conference on Image processing

作者： Ning Yan 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: (数字)9781728163956

ISBN: (纸本)9781728163963

In this paper, we consider a novel image coding paradigm, termed semantically scalable coding. In the new paradigm, coded bitstream serves for multiple different semantic analysis tasks, and different tasks require different semantic granularities of the image. Thus, the bitstream is designed to be scalable in the sense that progressive decoding of the bitstream provides coarse-to-fine semantic granularities. As a concrete example, we consider the task of coarse-grained and fine-grained image classification. We present a method to compress the multiple deep feature maps that are intermediate representations of an image passing a trained deep network. The deep-layer feature maps can serve for coarse-grained image classification while the shallow-layer feature maps can serve for fine-grained image classification. Experimental results demonstrate the feasibility of the proposed method, as well as the advantage of the semantically scalable coding paradigm.

关键词： Image coding Task analysis Semantics Visualization Correlation Decoding Encoding

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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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Three-Dimensional Point-Cloud Plus Patches: Towards Model-Based Image Coding in the Cloud

Three-Dimensional Point-Cloud Plus Patches: Towards Model-Ba...

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IEEE International Conference on Multimedia Big Data (BigMM)

作者： Dong Liu Tong Shao 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)9781479986897

In the era of cloud computing, there are many correlated images in the cloud, joint compression of these images may provide much higher compression ratio than individual coding. Model-based coding is an appealing approach to image coding in the cloud, as it removes knowledge redundancy among images that share the same model. In this paper, we make an attempt to model-based image coding for landmark images, where our model consists of three-dimensional (3-D) point-cloud plus image patches to describe the geometry and surface color of the landmark respectively. The camera parameters of an input image are estimated based on the 3-D point-cloud and the patches in the model, and then prediction image is generated by selecting, warping, and stitching image patches as well as illuminance compensation, the residue between original and prediction images is compressed by P-frame coding in HEVC encoder. We perform experiments on an Internet photo collection to verify the effectiveness of the proposed scheme. Preliminary results display the superior performance of our scheme that achieves as high as 39.9% bits saving compared to HEVC intra on a single image. The proposed scheme indicates a promising approach to image coding in the cloud and is worthy of in-depth investigation.

关键词： Image coding Solid modeling Cameras Decoding Redundancy Feature extraction Lighting

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Deep Learning-Based Nonlinear Transform for HEVC Intra Coding

Deep Learning-Based Nonlinear Transform for HEVC Intra Codin...

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IEEE Visual Communications and Image processing (VCIP)

作者： Kun Yang Dong Liu 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: (数字)9781728180687

ISBN: (纸本)9781728180694

In the hybrid video coding framework, transform is adopted to exploit the dependency within the input signal. In this paper, we propose a deep learning-based nonlinear transform for intra coding. Specifically, we incorporate the directional information into the residual domain. Then, a convolutional neural network model is designed to achieve better decorrelation and energy compaction than the conventional discrete cosine transform. This work has two main contributions. First, we propose to use the intra prediction signal to reduce the directionality in the residual. Second, we present a novel loss function to characterize the efficiency of the transform during the training. To evaluate the compression performance of the proposed transform, we implement it into the High Efficiency Video Coding reference software. Experimental results demonstrate that the proposed method achieves up to 1.79% BD-rate reduction for natural videos.

关键词： Transforms Discrete cosine transforms Neural networks Transform coding Video coding Image coding Decoding

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Convolutional Neural Network-Based Residue Super-Resolution for Video Coding

Convolutional Neural Network-Based Residue Super-Resolution ...

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IEEE Visual Communications and Image processing (VCIP)

作者： Kang Liu 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

Inspired by the progress of image and video super-resolution (SR) achieved by convolutional neural network (CNN), we propose a CNN-based residue SR method for video coding. Different from the previous works that operate in the pixel domain, i.e. down- and up-sampling of image or video frame, we propose to perform down- and up-sampling in the residue domain. Specifically, for each block, we perform motion estimation and compensation to achieve residual signal at the original resolution, then we down-sample the residue and compress it at low resolution, and perform residue SR using a trained CNN model. We design a new CNN for residue SR with the help of the motion compensated prediction signal. We integrate the residue SR method into the High Efficiency Video Coding (HEVC) scheme, providing mode decision at the level of coding tree unit. Experimental results show that our method achieves on average 4.0% and 2.8% BD-rate reduction under low-delay P and low-delay B configurations, respectively.

关键词： Encoding Signal resolution Video coding Spatial resolution Delays Convolution

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Efficient Integer-Arithmetic-Only Convolutional Networks with Bounded ReLU

Efficient Integer-Arithmetic-Only Convolutional Networks wit...

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IEEE International Symposium on Circuits and systems (IScas)

作者： Hengrui Zhao Dong Liu Houqiang Li CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei China

To facilitate large-scale deployment of convolutional networks, integer-arithmetic-only inference has been demonstrated effective, which not only reduces computational cost but also ensures cross-platform consistency. However, previous studies on integer networks usually report a decline in the inference accuracy, given the same number of parameters as floating-point-number (FPN) networks. In this paper, we propose to finetune and quantize a well-trained FPN convolutional network to obtain an integer convolutional network. Our key idea is to adjust the upper bound of a bounded rectified linear unit (ReLU), which replaces the normal ReLU and effectively controls the dynamic range of activations. Based on the tradeoff between learning ability and quantization error of networks, we managed to preserve full accuracy after quantization and obtain efficient integer networks. Our experiments on ResNet for image classification demonstrate that our 8-bit integer networks achieve state-of-the-art performance compared with Google's TensorFlow and NVIDIA's TensorRT. Moreover, we experiment on VDSR for image super-resolution and on VRCNN for compression artifact reduction, both of which serve regression tasks that natively require high inference accuracy. Besides ensuring the equivalent performance as the corresponding FPN networks, our integer networks have only 1/4 memory cost and run 2× faster on GPUs.

关键词： Upper bound Quantization (signal) Image coding Superresolution Dynamic range Task analysis Image classification

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Dominant Physical Scattering Mechanism Analysis for GF-3 Typical Ground Objects by Polarimetric Decomposition

Dominant Physical Scattering Mechanism Analysis for GF-3 Typ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jin Yan Qiu Xiaolan Lijia Huang Laboratory of Spatial Information Intelligent Processing System Institute of Electronics Chinese Academy of Sciences Suzhou China Key Laboratory of Technology in Geo-spatial Information Processing and Application System CAS

The dominant scattering mechanism is of great significance for the application of ground objects classification and target detection. It can also verify the quality of the polarimetric data by check the dominant scattering mechanism of known ground objects. In order to improve the application performance, this paper studies the dominant scattering mechanism of GF-3 typical ground objects based on a large number of data slices. The GF-3 fully polarimetric data slices are classified based on the MODIS global classification map, and the GF-3 slice library of typical ground objects is constructed. Based on large amounts of GF-3 samples, we carry out the statistical analysis of dominant scattering mechanism separation results for typical GF-3 ground objects (building, woodland, cultivated land, grassland and waters) of by means of h/alpha/A decomposition. The quantitative results reveal the polarimetric scattering feature of different ground objects, and provide reference for fully polarimetric SAR application.

关键词： Scattering Synthetic aperture radar Entropy Buildings Statistical analysis Radar polarimetry Sensors

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