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SSSIC: Semantics-to-Signal Scalable image coding With Learned Structural Representations

IEEE TRANSACTIONS ON image PROCESSING 2021年 30卷 8939-8954页

作者： Yan, Ning Gao, Changsheng Liu, Dong Li, Houqiang Li, Li Wu, Feng Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China Kwai Inc Hangzhou 310018 Peoples R China

We address the requirement of image coding for joint human-machine vision, i.e., the decoded image serves both human observation and machine analysis/understanding. Previously, human vision and machine vision have been extensively studied by image (signal) compression and (image) feature compression, respectively. Recently, for joint human-machine vision, several studies have been devoted to joint compression of images and features, but the correlation between images and features is still unclear. We identify the deep network as a powerful toolkit for generating structural image representations. From the perspective of information theory, the deep features of an image naturally form an entropy decreasing series: a scalable bitstream is achieved by compressing the features backward from a deeper layer to a shallower layer until culminating with the image signal. Moreover, we can obtain learned representations by training the deep network for a given semantic analysis task or multiple tasks and acquire deep features that are related to semantics. With the learned structural representations, we propose SSSIC, a framework to obtain an embedded bitstream that can be either partially decoded for semantic analysis or fully decoded for human vision. We implement an exemplar SSSIC scheme using coarse-to-fine image classification as the driven semantic analysis task. We also extend the scheme for object detection and instance segmentation tasks. The experimental results demonstrate the effectiveness of the proposed SSSIC framework and establish that the exemplar scheme achieves higher compression efficiency than separate compression of images and features.

关键词： image coding Feature extraction Task analysis Semantics Decoding image representation Scalability Feature compression image compression image representation machine vision scalable coding

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Light Field image coding Using VVC Standard and View Synthesis Based on Dual Discriminator GAN

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 2972-2985页

作者： Bakir, Nader Hamidouche, Wassim Fezza, Sid Ahmed Samrouth, Khouloud Deforges, Olivier Univ Rennes INSA Rennes CNRS IETR UMR F-6164 Rennes France Natl Inst Telecommun Oran 31000 Algeria ICT Oran 31000 Algeria Lebanese Univ Fac Engn Elect & Telecommun Dept Tripoli 1300 Lebanon

Light field (LF) technology is considered as a promising way for providing a high-quality virtual reality (VR) content. However, such an imaging technology produces a large amount of data requiring efficient LF image compression solutions. In this paper, we propose a LF image coding method based on a view synthesis and view quality enhancement techniques. Instead of transmitting all the LF views, only a sparse set of reference views are encoded and transmitted, while the remaining views are synthesized at the decoder side. The transmitted views are encoded using the versatile video coding (VVC) standard and are used as reference views to synthesize the dropped views. The selection of non-reference dropped views is performed using a rate-distortion optimization based on the VVC temporal scalability. The dropped views are reconstructed using the LF dual discriminator GAN (LF-D2GAN) model. In addition, to ensure that the quality of the views is consistent, at the decoder, a quality enhancement procedure is performed on the reconstructed views allowing smooth navigation across views. Experimental results show that the proposed method provides high coding performance and overcomes the state-of-the-art LF image compression methods by -36.22% in terms of BD-BR and 1.35 dB in BD-PSNR. The web page of this work is available at https://***/***.

关键词： image coding Encoding Cameras Decoding Transforms Transform coding Standards coding structure deep learning light field quality enhancement RDO VVC view synthesis

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image coding WITH NEURAL NETWORK-BASED COLORIZATION

IMAGE CODING WITH NEURAL NETWORK-BASED COLORIZATION

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Lopes, Diogo Ascenso, Joao Brites, Catarina Pereira, Fernando Univ Lisbon Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9781728176055

Automatic colorization is a process with the objective of inferring the color of grayscale images. This process is frequently used for artistic purposes and to restore the color in old or damaged images. Motivated by the excellent results obtained with deep learning-based solutions in the area of automatic colorization, this paper proposes an image coding solution integrating a deep learning-based colorization process to estimate the chrominance components based on the decoded luminance which is regularly encoded with a conventional image coding standard. In this case, the chrominance components are not coded and transmitted as usual, notably after some subsampling, as only some color hints, i.e. chrominance values for specific pixel locations, may be sent to the decoder to help it creating more accurate colorizations. To boost the colorization and final compression performance, intelligent ways to select the color hints are proposed. Experimental results show performance improvements with the increased level of intelligence in the color hints extraction process and a good subjective quality of the final decoded (and colorized) images.

关键词： Colorization neural networks image coding deep learning

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JPEG XS-A New Standard for Visually Lossless Low-Latency Lightweight image coding

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PROCEEDINGS OF THE IEEE 2021年第9期109卷 1559-1577页

作者： Descampe, Antonin Richter, Thomas Ebrahimi, Touradj Foessel, Siegfried Keinert, Joachim Bruylants, Tim Pellegrin, Pascal Buysschaert, Charles Rouvroy, Gael Univ Catholique Louvain UCLouvain B-1348 Louvain La Neuve Belgium Fraunhofer IIS D-91058 Erlangen Germany Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland IntoPIX SA B-1435 Mont St Guibert Belgium

Joint Photographic Experts Group (JPEG) XS is a new International Standard from the JPEG Committee (formally known as ISO/International Electrotechnical Commission (IEC) JTC1/SC29/WG1). It defines an interoperable, visually lossless low-latency lightweight image coding that can be used for mezzanine compression within any AV market. Among the targeted use cases, one can cite video transport over professional video links (serial digital interface (SDI), internet protocol (IP), and Ethernet), real-time video storage, memory buffers, omnidirectional video capture and rendering, and sensor compression (for example, in cameras and the automotive industry). The core coding system is composed of an optional color transform, a wavelet transform, and a novel entropy encoder, processing groups of coefficients by coding their magnitude level and packing the magnitude refinement. Such a design allows for visually transparent quality at moderate compression ratios, scalable end-to-end latency that ranges from less than one line to a maximum of 32 lines of the image, and a low-complexity real-time implementation in application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), central processing unit (CPU), and graphics processing unit (GPU). This article details the key features of this new standard and the profiles and formats that have been defined so far for the various applications. It also gives a technical description of the core coding system. Finally, the latest performance evaluation results of recent implementations of the standard are presented, followed by the current status of the ongoing standardization process and future milestones.

关键词： Transform coding Streaming media image coding Codecs Real-time systems Broadcasting Graphics processing units Video compression Broadcast codec image sensor compression standard uncompressed video

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Improving Quantization Matrices for image coding by Machine Learning 22

Improving Quantization Matrices for Image Coding by Machine ...

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6th International Conference on Digital Signal Processing, ICDSP 2022

作者： Ke, Wei Chan, Ka-Hou Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781450395809

We investigate the generation of quantization matrices for image coding in the scenario to balance compression ratio and quality. We make use of machine learning to train and determine those quantization matrices that can achieve the best compression ratio while reaching the quality settings. By introducing the trainable parameters and considering the impact of the quantization module on task performance and compression ratio, the DCT and quantization modules are jointly optimized to minimize the total coding cost. We evaluate the well-trained quantization matrices under various quality settings of JPEG. The results indicate that the proposed scheme can be combined with quality settings to consistently achieve better compression performance. © 2022 ACM.

关键词： image coding

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Compressed Sensing by Using Measurement Completion for Robust image coding 24

Compressed Sensing by Using Measurement Completion for Robus...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhang, Bo Xiao, Di Bai, Sen Li, Min Communication Nco Academy Army Engineering University Chongqing400035 China College of Computer Science Chongqing University Chongqing400044 China Chongqing Institute of Engineering Chongqing400056 China

ISBN: (纸本)9798350319934

Compressed sensing (CS) has been demonstrated to be an effective method for robust image coding. However, for existing CS-based image coding schemes, recovery performance drops rapidly at high packet loss rates (PLRs) because the received CS measurements are insufficient for stable recovery. To solve this problem, we propose a novel robust image coding scheme by using CS with measurement completion in this paper. By dividing the original image into a lot of down-sampled images with interweaving permutation (IP) and then sampling them with scrambled 2D CS (2DCS), we can obtain the CS measurement vectors of the down-sampled images. Since the CS measurement vectors preserve the correlation of the down-sampled images, they are also highly-correlated with each other. By exploring the correlation among the CS measurement vectors, a measurement completion strategy is proposed, which can recover many lost CS measurements due to packet loss at the decoder side. Simulation results show that the proposed scheme can significantly outperform previous CS-based image coding schemes at high PLRs in terms of rate distortion (R-D) performance. This advantage makes the proposed scheme a good candidate for those image communication systems which need to provide reliable transmission for the image data via channels with high PLRs. © 2022 IEEE.

关键词： image coding

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Performance Evaluation of the Block Truncation image coding with BCH Codes under Noisy Channels 19

Performance Evaluation of the Block Truncation Image Coding ...

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19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Lamsrichan, Poonlap Manthamkarn, Visuttha Tuntoolavest, Usana Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

The objective of this paper is to study the performance of block truncation coding and other color quantization algorithms under noisy channels. From the high efficiency variable bit rate BTC coders, some error-correcting bits of shortened BCH codes are added to make a constant bit rate coder. With the modified block, not only some channel distortion can be recovered but error propagating in the data stream is also decreased. The proposed TMM-BTC with shortened BCH codes yield better PSNR than other color quantization coders in a wide range of channel BER. © 2022 IEEE.

关键词： image coding

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Health Assessment for RUL Prediction of Machinery Components Using Low-Sampling Temporal Signals: A Condensed image coding Approach

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Han, Danyang Tang, Diyin Yu, Jinsong Wei, Zhenzhong Song, Yue Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

An effective health assessment guarantees the high accuracy of remaining useful life (RUL) prediction of machinery components. The key to health assessment is the health indicator (HI) of machinery components, which are generally constructed by feature fusion from the time and frequency domains in sensorial data. However, the existing HI construction methods are constrained due to the low-sampling rate and the susceptibility to environmental disturbances of sensorial data. To draw these issues, this article mainly proposes an HI construction method combining condensed image coding and a metrics-constrained deep learning model for improving the accuracy of RUL prediction. First, to extract more information in time series under a low-sampling rate, a recurrence plot-gray (RP-G) image coding is extended from RP images to extract and fuse both the global and local dynamics at multiple scales in time series. Then, a nested residual-convolution autoencoder (NR-CAE) model is proposed to extract degradation-related information from RP-G images containing information disturbed by the environment. Finally, to build a more solid connection between the image-based features and the RUL prediction, a metric-constrained gated recurrent unit (MC-GRU) is proposed by considering characteristics related to RUL prediction and temporal relationships in RP-G image-based features. A commercial case of wind turbine gearboxes in Liaoning, China, is investigated and demonstrates that the proposed method owns the ability to construct reliable HI of machinery components for accurate RUL prediction, using low-sampling sensorial data under environmental disturbances.

关键词： Health indicator (HI) construction image coding remaining useful life (RUL) prediction wind turbines

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Probability models for highly parallel image coding architecture

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SIGNAL PROCESSING-image COMMUNICATION 2023年 112卷

作者： Auli-Llinas, Francesc Bartrina-Rapesta, Joan Hernandez-Cabronero, Miguel Univ Autonoma Barcelona Dept Informat & Commun Engn Bellaterra 08193 Spain UAB Escola Engn Bellaterra 08193 Spain

A key aspect of image coding systems is the probability model employed to code the data. The more precise the probability estimates inferred by the model, the higher the coding efficiency achieved. In general, probability models adjust the estimates after coding every new symbol. The main difficulty to apply such a strategy to a highly parallel coding engine is that many symbols are coded simultaneously, so the probability adaptation requires a different approach. The strategy employed in previous works utilizes stationary estimates collected a priori from a training set. Its main drawback is that statistics are dependent of the image type, so different images require different training sets. This work introduces two probability models for a highly parallel architecture that, similarly to conventional systems, adapt probabilities while coding data. One of the proposed models estimates probabilities through a finite state machine, while the other employs the statistics of already coded symbols via a sliding window. Experimental results indicate that the latter approach improves the performance achieved by the other models, including that of JPEG2000 and High Throughput JPEG2000, at medium and high rates with only a slight increase in computational complexity.

关键词： image coding Parallel computing Entropy coding Probability models

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The Gap Between Principle and Practice of Lossy image coding

arXiv

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arXiv 2025年

作者： Zhang, Haotian Liu, Dong MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei230093 China

Lossy image coding is the art of computing that is principally bounded by the image’s rate-distortion function. This bound, though never accurately characterized, has been approached practically via deep learning technologies in recent years. Indeed, learned image coding schemes allow direct optimization of the joint rate-distortion cost, thereby outperforming the handcrafted image coding schemes by a large margin. Still, it is observed that there is room for further improvement in the rate-distortion performance of learned image coding. In this article, we identify the gap between the ideal rate-distortion function forecasted by Shannon’s information theory and the empirical rate-distortion function achieved by the state-of-the-art learned image coding schemes, revealing that the gap is incurred by five different effects: modeling effect, approximation effect, amortization effect, digitization effect, and asymptotic effect. We design simulations and experiments to quantitively evaluate the last three effects, which demonstrates the high potential of future lossy image coding technologies. Copyright © 2025, The Authors. All rights reserved.

关键词： image coding

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