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Video Compression based on Jointly Learned Down-Sampling and Super-Resolution Networks

Video Compression based on Jointly Learned Down-Sampling and...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuzhuo Wei Li Chen Li Song Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728173221

With the blooming of deep learning technology in computer vision, the integration of deep learning and the traditional video coding has made significant improvements, especially applying the super-resolution neural network as the post-processing module in the down-sampling-based video compression framework. However, the pre-processing module lacks back-propagated gradients for jointly considering down-sampling and up-sampling due to the non-differentiability of the traditional video codec. In this paper, we propose an end- to-end down-sampling-based video compression framework applying convolutional neural networks both as down-sampling and upsampling. We use a virtual codec neural network to approximate the actual video codec so that the gradient can be effectively back-propagated for joint training. Experimental results show the superiority of our proposed framework compared with the predefined down-sampling-based video compression and various methods of joint training.

关键词： Training Deep learning Video coding image coding visual communication Superresolution Neural networks

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SMRD: A Local Feature Descriptor for Multi-modal image Registration

SMRD: A Local Feature Descriptor for Multi-modal Image Regis...

引用

IEEE visual communications and image processing (VCIP)

作者： Jiayu Xie Xin Jin Hongkun Cao Shenzhen Key Lab of Broadband Network and Multimedia Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9781728173221

image registration among multimodality has received increasing attention in the scope of computer vision and computational photography nowadays. However, the non-linear intensity variations prohibit the accurate feature points matching between modal-different image pairs. Thus, a robust image descriptor for multi-modal image registration is proposed, named shearlet-based modality robust descriptor(SMRD). The anisotropic feature of edge and texture information in multi-scale is encoded to describe the region around a point of interest based on discrete shearlet transform. We conducted the experiments to verify the proposed SMRD compared with several state-of-the-art multi-modal/multispectral descriptors on four different multi-modal datasets. The experimental results showed that our SMRD achieves superior performance than other methods in terms of precision, recall and F1-score.

关键词： Photography image registration Computer vision visual communication image edge detection Transforms Robustness

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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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Large-Scale Crowdsourcing Subjective Quality Evaluation of Learning-Based image Coding

Large-Scale Crowdsourcing Subjective Quality Evaluation of L...

引用

IEEE visual communications and image processing (VCIP)

作者： Evgeniy Upenik Michela Testolina João Ascenso Fernando Pereira Touradj Ebrahimi Multimedia Signal Processing Group - Ecole Polytechnique Fédérale de Lausanne Instituto de Telecornunicações - Instituto Superior Técnico

ISBN: (纸本)9781728173221

Learning-based image codecs produce different compression artifacts, when compared to the blocking and blurring degradation introduced by conventional image codecs, such as JPEG, JPEG 2000 and HEIC. In this paper, a crowdsourcing based subjective quality evaluation procedure was used to benchmark a representative set of end-to-end deep learning-based image codecs submitted to the MMSP'2020 Grand Challenge on Learning-Based image Coding and the JPEG AI Call for Evidence. For the first time, a double stimulus methodology with a continuous quality scale was applied to evaluate this type of image codecs. The subjective experiment is one of the largest ever reported including more than 240 pair-comparisons evaluated by 118 naïve subjects. The results of the benchmarking of learning-based image coding solutions against conventional codecs are organized in a dataset of differential mean opinion scores along with the stimuli and made publicly available.

关键词： Crowdsourcing Degradation image coding Codecs visual communication Transform coding Benchmark testing

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Pixel Gradient Based Zooming Method for Plenoptic Intra Prediction

Pixel Gradient Based Zooming Method for Plenoptic Intra Pred...

引用

IEEE visual communications and image processing (VCIP)

作者： Fan Jiang Xin Jin Kedeng Tong Shenzhen Key Lab of Broadband Network and Multimedia Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9781728173221

Plenoptic 2.0 videos that record time-varying light fields by focused plenoptic cameras are prospective to immersive visual applications due to capturing dense sampled light fields with high spatial resolution in the rendered sub-apertures. In this paper, an intra prediction method is proposed for compressing multi-focus plenoptic 2.0 videos efficiently. Based on the estimation of zooming factor, novel gradient-feature-based zooming, adaptive-bilinear-interpolation-based tailoring and inverse-gradient-based boundary filtering are proposed and executed sequentially to generate accurate prediction candidates for weighted prediction working with adaptive skipping strategy. Experimental results demonstrate the superior performance of the proposed method relative to HEVC and state-of-the-art methods.

关键词： Video coding visualization image coding Filtering visual communication Estimation Prediction methods

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A Video Dataset for Learning-based visual Data Compression and Analysis

A Video Dataset for Learning-based Visual Data Compression a...

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IEEE visual communications and image processing (VCIP)

作者： Xiaozhong Xu Shan Liu Zeqiang Li Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781728173221

Learning-based visual data compression and analysis have attracted great interest from both academia and industry recently. More training as well as testing datasets, especially good quality video datasets are highly desirable for related research and standardization activities. A UHD video dataset, referred to as Tencent Video Dataset (TVD), is established to serve various purposes such as training neural network-based coding tools and testing machine vision tasks including object detection and segmentation. This dataset contains 86 video sequences with a variety of content coverage. Each video sequence consists of 65 frames at 4K (3840x2160) spatial resolution. In this paper, the details of this dataset, as well as its performance when compressed by VVC and HEVC video codecs, are introduced.

关键词： Training visualization image coding Video sequences Object segmentation Standardization Object detection

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Nearly Reversible image-to-image Translation Using Joint Inter-Frame Coding and Embedding

Nearly Reversible Image-to-Image Translation Using Joint Int...

引用

IEEE visual communications and image processing (VCIP)

作者： Xinzhu Cao Yuanzhi Yao Nenghai Yu University of Science and Technology of China Hefei China

ISBN: (纸本)9781728173221

image-to-image translation tasks which have been widely investigated with generative adversarial networks (GAN) aim to map an image from the source domain to the target domain. The translated image can be inversely mapped to the reconstructed source image. However, existing GAN-based schemes lack the ability to accomplish reversible translation. To remedy this drawback, a nearly reversible image-to-image translation scheme where the reconstructed source image is approximately distortion-free compared with the corresponding source image is proposed in this paper. The proposed scheme jointly considers inter-frame coding and embedding. Firstly, we organize the GAN-generated reconstructed source image and the source image into a pseudo video. Furthermore, the bitstream obtained by inter-frame coding is reversibly embedded in the translated image for nearly lossless source image reconstruction. Extensive experimental results and analysis demonstrate that the proposed scheme can achieve a high level of performance in image quality and security.

关键词： image quality image coding visual communication Generative adversarial networks Security Task analysis image reconstruction

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MPEG Immersive Video tools for Light Field Head Mounted Displays

MPEG Immersive Video tools for Light Field Head Mounted Disp...

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IEEE visual communications and image processing (VCIP)

作者： Daniele Bonatto Grégoire Hirt Alexander Kvasov Sarah Fachada Gauthier Lafruit Laboratories of Image Synthesis and Analysis Université Libre de Bruxelles Brussels Belgium CREAL Lausanne Switzerland

ISBN: (纸本)9781728173221

Light field displays project hundreds of micro-parallax views for users to perceive 3D without wearing glasses. It results in gigantic bandwidth requirements if all views would be transmitted, even using conventional video compression per view. MPEG Immersive Video (MIV) follows a smarter strategy by transmitting only key images and some metadata to synthesize all the missing views. We developed (and will demonstrate) a real-time Depth image Based Rendering software that follows this approach for synthesizing all light field micro-parallax views from a couple of RGBD input views.

关键词： Three-dimensional displays visual communication Transform coding Streaming media Video compression Metadata Rendering (computer graphics)

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Spatio-spectral image Reconstruction Using Non-local Filtering

Spatio-spectral Image Reconstruction Using Non-local Filteri...

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IEEE visual communications and image processing (VCIP)

作者： Frank Sippel Jürgen Seiler André Kaup Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nürnberg (FAU) Erlangen Germany

ISBN: (纸本)9781728173221

In many image processing tasks it occurs that pixels or blocks of pixels are missing or lost in only some channels. For example during defective transmissions of RGB images, it may happen that one or more blocks in one color channel are lost. Nearly all modern applications in image processing and transmission use at least three color channels, some of the applications employ even more bands, for example in the infrared and ultraviolet area of the light spectrum. Typically, only some pixels and blocks in a subset of color channels are distorted. Thus, other channels can be used to reconstruct the missing pixels, which is called spatio-spectral reconstruction. Current state-of-the-art methods purely rely on the local neighborhood, which works well for homogeneous regions. However, in high-frequency regions like edges or textures, these methods fail to properly model the relationship between color bands. Hence, this paper introduces non-local filtering for building a linear regression model that describes the inter-band relationship and is used to reconstruct the missing pixels. Our novel method is able to increase the PSNR on average by 2 dB and yields visually much more appealing images in high-frequency regions.

关键词： image color analysis Filtering visual communication image edge detection Linear regression Buildings Reconstruction algorithms

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DFTS2: Deep Feature Transmission Simulation for Collaborative Intelligence

DFTS2: Deep Feature Transmission Simulation for Collaborativ...

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IEEE visual communications and image processing (VCIP)

作者： Ashiv Dhondea Robert A. Cohen Ivan V. Bajić School of Engineering Science Simon Fraser University Burnaby BC Canada

ISBN: (纸本)9781728173221

In edge-cloud collaborative intelligence (CI), an unreliable transmission channel exists in the information path of the AI model performing the inference. It is important to be able to simulate the performance of the CI system across an imperfect channel in order to understand system behavior and develop appropriate error control strategies. In this paper we present a simulation framework called DFTS2, which enables researchers to define the components of the CI system in TensorFlow 2, select a packet-based channel model with various parameters, and simulate system behavior under various channel conditions and error/loss control strategies. Using DFTS2, we also present the most comprehensive study to date of the packet loss concealment methods for collaborative image classification models.

关键词： image coding Monte Carlo methods visual communication Neural networks Packet loss Collaboration Propagation losses

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