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Efficient background picture coding for videos obtained from static cameras

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Efficient background picture coding for videos obtained from...

IEEE Visual Communications and Image processing (VCIP)

作者： Fangdong Chen Li Li Dong Liu Houqiang Li Zhuoyi Lv Haitao Yang CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei China Huawei Technologies Co. Ltd. Shenzhen China

With the exponential growth of surveillance videos, conference videos and sports videos, videos with static cameras present an unprecedented challenge for high-efficiency video coding technology. The existing schemes developed for these videos mostly encode the background as the long-term reference (LTR) to further improve the coding efficiency. However, since the bit allocation of the long-term background reference is not intensively studied, the coding efficiency is still unsatisfactory. Based on the stability analysis of the video content, an efficient background picture coding algorithm for videos obtained from static cameras, which is embedded with the basic unit level bit allocation, is proposed in this paper. Experimental results reveal that on top of the default mode in HEVC, our method offers the performance with 10.8% BD-rate reduction on average. Compared with the state-of-the-art algorithm, it still outperforms for kinds of test sequences with negligible increases of computational complexity in both encoder and decoder.

关键词： Encoding Videos Cameras Distortion Stability analysis Video coding Delays

Object Segmentation-Assisted Inter Prediction for Versatile Video Coding

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arXiv 2024年

作者： Li, Zhuoyuan Yuan, Zikun Li, Li Liu, Dong Tang, Xiaohu Wu, Feng CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Information Security and National Computing Grid Laboratory Southwest Jiaotong University Chengdu610031 China

In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, resulting in complex motion fields that are difficult to represent compactly. This problem has been tackled by more flexible block partitioning methods in the Versatile Video Coding (VVC) standard, but the more flexible partitions require more overhead bits to signal and still cannot be made arbitrarily shaped. To address this limitation, we propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies. With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions without any extra signal. Using the segmentation mask, motion compensation is separately performed for different regions, achieving higher prediction accuracy. The segmentation mask is further used to code the motion vectors of different regions more efficiently. Moreover, the segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences, under the Low-delay P, Low-delay B, and Random Access configurations, respectively. Copyright © 2024, The Authors. All rights reserved.

关键词： Video signal processing

Dem Extraction Using C-Band Circular Sar Data

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Dem Extraction Using C-Band Circular Sar Data

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shanshan Feng Yun Lin Wen Hong Bing Han Yanping Wang Yanhui Yang Wenjie Shen Fei Teng Key Laboratory of Technology in Geospatial Information Processing and Application System Chinese Academy of Sciences Beijing China North China University of Technology Beijing China Beijing Institute of Remote Sensing Equipment Beijing China

Circular Synthetic Aperture Radar(CSAR) has become a hotspot with its characteristic of elevation plane resolution and all-aspect observing ability. Digital elevation model (DEM) extraction in urban arears by using single-pass CSAR data without requiring additional knowledge is a subject of interest. The target, whose real height is not equal to the reference imaging height will project to different locations after imaging in different sub-aperture. In this paper, the quantitative relationship between offset of imaging points and height difference is deduced theoretically in the real scene, where the airborne SAR platform trajectory is not a standard circle. DEM of an area is presented using the data acquired by the Institute of Electronics, Chinese Academy of Sciences (IEcas). Compared with the DEM provided by the German Aerospace Center (DLR) with 1m absolute height error, the effectiveness of the proposed method is verified.

关键词： Synthetic aperture radar Imaging Azimuth Trajectory Radar polarimetry Data mining Geometry

Fast algorithm of arbitrary factor subpixel downsampling based on frequency analysis

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Fast algorithm of arbitrary factor subpixel downsampling bas...

IEEE International Conference on Multimedia and Expo (ICME)

作者： Ketan Tang Oscar C. Au Lu Fang Jiahao Pang Yuanfang Guo Jiali Li The Hong Kong University of Science and Technology CAS Key Laboratory of Technology in Geo-spatial Information Processingand Application System University of Science and Technology of China

Subpixel-based downsampling has shown its advantages over pixel-based downsampling in terms of preserving more spatial details along edges and generating sharper images, at the cost of certain amount of color-fringing artifacts in the downsampled image. To balance the sharpness and color-fringing artifacts, some algorithms are proposed to design optimal anti-aliasing (AA) filters, which are either image independent, or computationally too expensive. And all of the existing AA filters are designed for fixed downsampling factor, which makes them impractical for real applications. In this paper we propose two fast algorithms to design AA filter for arbitrary factor subpixel downsampling based on frequency analysis of the input image. The proposed algorithms generate image dependent AA filter which is as good as the state-of-the-art algorithm, but much faster.

关键词： Algorithm design and analysis Cutoff frequency Shape Frequency-domain analysis Interpolation Signal processing algorithms Time measurement

Sequential reinforced 360-degree video adaptive streaming with cross-user attentive network

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arXiv 2020年

作者： Fu, Jun Chen, Zhibo Chen, Xiaoming Li, Weiping CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China School of Computer Science and Engineering Beijing Technology and Business University China

In the tile-based 360-degree video streaming, predicting user’s future viewpoints and developing adaptive bitrate (ABR) algorithms are essential for optimizing user’s quality of experience (QoE). Traditional single-user based viewpoint prediction methods fail to achieve good performance in long-term prediction, and the recently proposed reinforcement learning (RL) based ABR schemes applied in traditional video streaming can not be directly applied in the tile-based 360-degree video streaming due to the exponential action space. Therefore, we propose a sequential reinforced 360-degree video streaming scheme with cross-user attentive network. Firstly, considering different users may have the similar viewing preference on the same video, we propose a cross-user attentive network (CUAN), boosting the performance of long-term viewpoint prediction by selectively utilizing cross-user information. Secondly, we propose a sequential RL-based (360SRL) ABR approach, transforming action space size of each decision step from exponential to linear via introducing a sequential decision structure. We evaluate the proposed CUAN and 360SRL using trace-driven experiments and experimental results demonstrate that CUAN and 360SRL outperform existing viewpoint prediction and ABR approaches with a noticeable margin. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

Cut-and-Fold: Automatic 3D modeling from a single image

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Cut-and-Fold: Automatic 3D modeling from a single image

IEEE International Conference on Multimedia and Expo Workshops (ICMEW)

作者： Guihang Wang Xuejin Chen Si Chen CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei Anhui China University of California San Diego La Jolla California USA

ISBN: (纸本)9781479947164

This paper presents a novel approach to automatically generate a plausible 3D model from a single outdoor image. Outdoor scene can be modeled as a set of planar billboards such as ground, sky and vertical trees or buildings. This Cut-and-Fold system takes the advantages of the statistical classification method for high-level geometric labeling of the image regions and the automatic image segmentation algorithm for detailed boundaries of the geometric regions to generate accurate geometric labeling and realistic rendering of novel views. Taking a single image as input, our method first roughly classifies each sub-region to the geometric categories: vertical, ground or sky. Then image segmentation algorithm is employed to refine the labeling results and generate accurate boundaries for the geometric regions. After cutting the image into several regions with their geometric labels, the system folds the regions into 3D billboards with accurate boundaries. The experimental results demonstrate that our method is capable of creating plausible 3D models automatically for a wide range of outdoor scenes.

关键词： Image segmentation Abstracts Labeling Indium phosphide Three-dimensional displays Solid modeling

An efficient four-parameter affine motion model for video coding

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arXiv 2017年

作者： Li, Li Li, Houqiang Liu, Dong Yang, Haitao Lin, Sixin Chen, Huanbang Wu, Feng CAS Key Laboratory of Technology Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China Media Technology Laboratory Central Research Institute Huawei Technologies Co. Ltd

In this paper, we study a simplified affine motion model based coding framework to overcome the limitation of translational motion model and maintain low computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., advanced affine motion vector prediction combined with a gradient-based fast affine motion estimation algorithm and affine model merge, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation, which reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation, which performs motion compensation at the block level rather than the pixel level to reduce the interpolation times. Our proposed techniques have been implemented based on the state-of-the-art high efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve on average 11.1% and 19.3% bits saving for random access and low delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both encoder and decoder are within an acceptable range. Copyright © 2017, The Authors. All rights reserved.

关键词： Motion estimation

A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective

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arXiv 2021年

作者： Li, Xin Jin, Xin Fu, Jun Yu, Xiaoyuan Tong, Bei Chen, Zhibo The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Eastern Institute for Advanced Study China Huawei Cloud China

Collecting amounts of distorted/clean image pairs in the real world is non-trivial, which seriously limits the practical applications of these supervised learning-based methods on real-world image super-resolution (RealSR). Previous works usually address this problem by leveraging unsupervised learning-based technologies to alleviate the dependency on paired training samples. However, these methods typically suffer from unsatisfactory texture synthesis due to the lack of supervision of clean images. To overcome this problem, we are the first to have a close look at the under-explored direction for RealSR, i.e., few-shot real-world image super-resolution, which aims to tackle the challenging RealSR problem with few-shot distorted/clean image pairs. Under this brand-new scenario, we propose Distortion Relation guided Transfer Learning (DRTL) for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions (i.e., synthetic distortions) to the target RealSR under the guidance of distortion relation. Concretely, DRTL builds a knowledge graph to capture the distortion relation between auxiliary distortions and target distortion (i.e., real distortions in RealSR). Based on the distortion relation, DRTL adopts a gradient reweighting strategy to guide the knowledge transfer process between auxiliary distortions and target distortions. In this way, DRTL could quickly learn the most relevant knowledge from the synthetic distortions for the target distortion. We instantiate DRTL with two commonly-used transfer learning paradigms, including pre-training and meta-learning pipelines, to realize a distortion relation-aware Few-shot RealSR. Extensive experiments on multiple benchmarks and thorough ablation studies demonstrate the effectiveness of our DRTL. Copyright © 2021, The Authors. All rights reserved.

关键词： Textures

Towards Hybrid-Optimization Video Coding

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arXiv 2022年

作者： Huo, Shuai Liu, Dong Li, Li Ma, Siwei Wu, Feng Gao, Wen The Cas Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China The Institute of Digital Media Peking University Beijing100871 China

Video coding is a mathematical optimization problem of rate and distortion essentially. To solve this complex optimization problem in practice, two popular video coding frameworks have been developed: block-based hybrid video coding and end-to-end learned video coding. If we rethink video coding from the perspective of optimization, we find that the existing two frameworks represent two directions of optimization solutions. Block-based hybrid video coding represents the discrete optimization solution because those irrelevant coding modes are discrete in mathematics. The discrete solution provides multiple starting points (i.e. modes) in global optimization space and then searches for the best one among them. However, the search-based optimization algorithm is not efficient enough. On the other hand, end-to-end learned video coding represents the continuous optimization solution because the optimization algorithm of deep learning, gradient descent, is based on a continuous function. The continuous solution optimizes a group of model parameters efficiently by such a numerical algorithm. However, limited by only one starting point, it is easy to fall into the local optimum. To better solve the optimization problem, we propose a hybrid of discrete and continuous optimization video coding. We regard video coding as a hybrid of the discrete and continuous optimization problem, and use both search and numerical algorithm to solve it. Our idea is to provide multiple discrete starting points in the global space and optimize the local optimum around each point by numerical algorithm efficiently. Finally, we search for the global optimum among those local optimums. Guided by the hybrid optimization idea, we design a hybrid optimization video coding framework, which is built on continuous deep networks entirely and also contains some discrete modes. We conduct a comprehensive set of experiments to verify the efficiency of our hybrid optimization. Compared to the continuous opti

关键词： Deep neural networks