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Analyzing Time Complexity of Practical Learned image Compression Models

Analyzing Time Complexity of Practical Learned Image Compres...

引用

IEEE visual communications and image processing (VCIP)

作者： Xiaohan Pan Zongyu Guo 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

We have witnessed the rapid development of learned image compression (LIC). The latest LIC models have outperformed almost all traditional image compression standards in terms of rate-distortion (RD) performance. However, the time complexity of LIC model is still underdiscovered, limiting the practical applications in industry. Even with the acceleration of GPU, LIC models still struggle with long coding time, especially on the decoder side. In this paper, we analyze and test a few prevailing and representative LIC models, and compare their complexity with traditional codecs including H.265/HEVC intra and H.266/VVC intra. We provide a comprehensive analysis on every module in the LIC models, and investigate how bitrate changes affect coding time. We observe that the time complexity bottleneck mainly exists in entropy coding and context modelling. Although this paper pay more attention to experimental statistics, our analysis reveals some insights for further acceleration of LIC model, such as model modification for parallel computing, model pruning and a more parallel context model.

关键词： Analytical models image coding Codecs visual communication Computational modeling Rate-distortion Decoding

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Deep Learning-Based Blind image Super-Resolution using Iterative Networks

Deep Learning-Based Blind Image Super-Resolution using Itera...

引用

IEEE visual communications and image processing (VCIP)

作者： Asfand Yaar Hasan F. Ates Bahadir K. Gunturk School of Engineering and Natural Sciences Istanbul Medipol University Turkey

ISBN: (纸本)9781728173221

Deep learning-based single image super-resolution (SR) consistently shows superior performance compared to the traditional SR methods. However, most of these methods assume that the blur kernel used to generate the low-resolution (LR) image is known and fixed (e.g. bicubic). Since blur kernels involved in real-life scenarios are complex and unknown, per-formance of these SR methods is greatly reduced for real blurry images. Reconstruction of high-resolution (HR) images from randomly blurred and noisy LR images remains a challenging task. Typical blind SR approaches involve two sequential stages: i) kernel estimation; ii) SR image reconstruction based on estimated kernel. However, due to the ill-posed nature of this problem, an iterative refinement could be beneficial for both kernel and SR image estimate. With this observation, in this paper, we propose an image SR method based on deep learning with iterative kernel estimation and image reconstruction. Simulation results show that the proposed method outperforms state-of-the-art in blind image SR and produces visually superior results as well.

关键词： Deep learning visual communication Simulation Superresolution Estimation Noise measurement Iterative methods

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Mixed-precision Quantization with Dynamical Hessian Matrix for Object Detection Network

Mixed-precision Quantization with Dynamical Hessian Matrix f...

引用

IEEE visual communications and image processing (VCIP)

作者： Zerui Yang Wen Fei Wenrui Dai Chenglin Li Junni Zou Hongkai Xiong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9781728173221

Mixed-precision quantization with adaptive bitwidth allocation for neural network has achieved higher compression rate and accuracy in classification task. However, it has not been well explored for object detection networks. In this paper, we propose a novel mixed-precision quantization scheme with dynamical Hessian matrix for object detection networks. We iteratively select a layer with the lowest sensitivity based on the Hessian matrix and downgrade its precision to reach the required compression ratio. The L-BFGS algorithm is utilized for updating the Hessian matrix in each quantization iteration. Moreover, we specifically design the loss function for objection detection networks by jointly considering the quantization effects on classification and regression loss. Experimental results on RetinaNet and Faster R-CNN show that the proposed DHMQ achieves state-of-the-art performance for quantized object detec-tors.

关键词： Quantization (signal) image coding Sensitivity visual communication Heuristic algorithms Object detection Detectors

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See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recovery from a Single Rainy image

See SIFT in a Rain: Divide-and-conquer SIFT Key Point Recove...

引用

IEEE visual communications and image processing (VCIP)

作者： Ping Wang Wei Wu Zhu Li Yong Liu State Key Laboratory of Integrated Services Networks Xidian University Xi'an China University of Missouri Kansas City USA

ISBN: (纸本)9781728173221

Scale-Invariant Feature Transform (SIFT) is one of the most well-known image matching methods, which has been widely applied in various visual fields. Because of the adoption of a difference of Gaussian (DoG) pyramid and Gaussian gradient information for extrema detection and description, respectively, SIFT achieves accurate key points and thus has shown excellent matching results but except under adverse weather conditions like rain. To address the issue, in the paper we propose a divide-and-conquer SIFT key points recovery algorithm from a single rainy image. In the proposed algorithm, we do not aim to improve quality for a derained image, but divide the key point recovery problem from a rainy image into two sub-problems, one being how to recover the DoG pyramid for the derained image and the other being how to recover the gradients of derained Gaussian images at multiple scales. We also propose two separate deep learning networks with different losses and structures to recover them, respectively. This divide-and-conquer scheme to set different objectives for SIFT extrema detection and description leads to very robust performance. Experimental results show that our proposed algorithm achieves state-of-the-art performances on widely used image datasets in both quantitative and qualitative tests.

关键词： Deep learning visualization Rain visual communication image processing image matching Dogs

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Distortion-based Neural Network for Compression Artifacts Reduction in VVC

Distortion-based Neural Network for Compression Artifacts Re...

引用

IEEE visual communications and image processing (VCIP)

作者： Jian Qian Hongkui Wang Li Yu Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781728173221

To reduce compression artifacts in video coding, prior knowledge from the codec is often utilized by deep learning based enhancement methods. However, most existing algorithms only take limited kinds of prior knowledge into consideration and directly feed it into neural networks, resulting in finite information obtained by the enhancement module. In this paper, a distortion-based neural network is proposed to better take advantage of features from the codec and further facilitate the quality of decoded videos. Firstly, a variety of prior knowledge is unitedly exploited to estimate the compression distortion. Secondly, an estimation module is also designed for the information obtained from the codec to improve the precision of estimation. With the accurately estimated distortion level, the enhancement module in the proposed method could reduce the corresponding artifacts by flexibly controlling the filtering strength. Moreover, the proposed method is integrated into VTM-7.1, achieving on average 5.92% BD-rate saving for Y component under All Intra configuration.

关键词： Knowledge engineering Codecs image coding visual communication Neural networks Estimation Distortion

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Telemoji: A video chat with automated recognition of facial expressions

Telemoji: A video chat with automated recognition of facial ...

引用

IEEE visual communications and image processing (VCIP)

作者： Alex Kreinis Tom Damri Tomer Leon Marina Litvak Irina Rabaev Shamoon College of Engineering Israel

ISBN: (纸本)9781728173221

Autism spectrum disorder (ASD) is frequently ac-companied by impairment in emotional expression recognition, and therefore individuals with ASD may find it hard to interpret emotions and interact. Inspired by this fact, we developed a web-based video chat to assist people with ASD, both for real-time recognition of facial emotions and for practicing. This real-time application detects the speaker's face in a video stream and classifies the expressed emotion into one of the seven categories: neutral, surprise, happy, angry, disgust, fear, and sad. The classification is then displayed as the text label below the speaker's face. We developed this application as a part of the undergraduate project for the ***. degree in Software Engineering. Its development and testing were made with the cooperation of the local society for children and adults with autism. The application has been released for unrestricted use on https://***/. The demo is available at http://***/telemojishortdemoblur.

关键词： Emotion recognition Autism image recognition visual communication Face recognition Streaming media Real-time systems

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Nanopore Sequencing Simulator for DNA Data Storage

Nanopore Sequencing Simulator for DNA Data Storage

引用

IEEE visual communications and image processing (VCIP)

作者： Eva Gil San Antonio Thomas Heinis Louis Carteron Melpomeni Dimopoulou Marc Antonini Laboratoire I3S UMR 7271 Université Côte d'Azur CNRS Sophia Antipolis France Imperial College London London United Kingdom

ISBN: (纸本)9781728173221

The exponential increase of digital data and the limited capacity of current storage devices have made clear the need for exploring new storage solutions. Thanks to its biological properties, DNA has proven to be a potential candidate for this task, allowing the storage of information at a high density for hundreds or even thousands of years. With the release of nanopore sequencing technologies, DNA data storage is one step closer to become a reality. Many works have proposed solutions for the simulation of this sequencing step, aiming to ease the development of algorithms addressing nanopore-sequenced reads. However, these simulators target the sequencing of complete genomes, whose characteristics differ from the ones of synthetic DNA. This work presents a nanopore sequencing simulator targeting synthetic DNA on the context of DNA data storage.

关键词： Sequential analysis visualization image coding visual communication DNA Memory Genomics

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Analysis of VVC Intra Prediction Block Partitioning Structure

Analysis of VVC Intra Prediction Block Partitioning Structur...

引用

IEEE visual communications and image processing (VCIP)

作者： Mário Saldanha Gustavo Sanchez César Marcon Luciano Agostini Federal University of Pelotas-Graduate Program in Computing (PPGC) Pelotas Brazil IF Farroupilha Alegrete Brazil Pontificial Catholic University of Rio Grande do Sul- Porto Alegre Brazil

ISBN: (纸本)9781728173221

This paper presents an encoding time and encoding efficiency analysis of the Quadtree with nested Multi-type Tree (QTMT) structure in the Versatile Video Coding (VVC) intra-frame prediction. The QTMT structure enables VVC to improve the compression performance compared to its predecessor standard at the cost of a higher encoding complexity. The intra-frame prediction time raised about 26 times compared to the HEVC reference software, and most of this time is related to the new block partitioning structure. Thus, this paper provides a detailed description of the VVC block partitioning structure and an in-depth analysis of the QTMT structure regarding coding time and coding efficiency. Based on the presented analyses, this paper can guide outcoming works focusing on the block partitioning of the VVC intra-frame prediction.

关键词： Video coding image coding Costs visual communication Focusing Software Computational complexity

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Two Stage Optimal Bit Allocation for HEVC Hierarchical Coding Structure

Two Stage Optimal Bit Allocation for HEVC Hierarchical Codin...

引用

IEEE visual communications and image processing (VCIP)

作者： Zizheng Liu Zhenzhong Chen Shan Liu School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Media Lab Palo AltoCAUSA

ISBN: (纸本)9781728173221

In this paper, we propose a two stage optimal bit allocation scheme for HEVC hierarchical coding structure. The two stage, i.e., the frame-level and the CTU-level bit allocation, are separately conducted in the traditional rate control methods. In our proposed method, the optimal allocation in the second stage is firstly considered, and then the allocation strategy in the second stage is deemed as a foreknowledge in the first stage and applied to guide the frame-level bit allocation. With the formulation, the two stage bit allocation problem can be converted to a joint optimization problem. By solving the formulated optimization problem, the two stage optimal bit allocation scheme is established, in which more appropriate number of bits can be allocated to each frame and each CTU. The experimental results show that our proposed method can bring higher coding efficiency while satisfying the constraint of bit rate precisely.

关键词： image coding visual communication Bit rate Rate-distortion Process control Resource management Optimization

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Learning in Compressed Domain for Faster Machine Vision Tasks

Learning in Compressed Domain for Faster Machine Vision Task...

引用

IEEE visual communications and image processing (VCIP)

作者： Jinming Liu Heming Sun Jiro Katto Waseda University Tokyo Japan Waseda Research Institute for Science and Engineering Waseda University Tokyo Japan JST PRESTO Kawaguchi Saitama Japan

ISBN: (纸本)9781728173221

Learned image compression (LIC) has illustrated good ability for reconstruction quality driven tasks (e.g. PSNR, MS-SSIM) and machine vision tasks such as image understanding. However, most LIC frameworks are based on pixel domain, which requires the decoding process. In this paper, we develop a learned compressed domain framework for machine vision tasks. 1) By sending the compressed latent representation directly to the task network, the decoding computation can be eliminated to reduce the complexity. 2) By sorting the latent channels by entropy, only selective channels will be transmitted to the task network, which can reduce the bitrate. As a result, compared with the traditional pixel domain methods, we can reduce about 1/3 multiply-add operations (MACs) and 1/5 inference time while keeping the same accuracy. Moreover, proposed channel selection can contribute to at most 6.8% bitrate saving.

关键词： image coding visual communication Machine vision Bit rate Entropy Decoding Task analysis

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