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Optimization of image Transmission in Cooperative Semantic Communication Networks

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第2期23卷 861-873页

作者： Zhang, Wenjing Wang, Yining Chen, Mingzhe Luo, Tao Niyato, Dusit Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Network Beijing 100876 Peoples R China Univ Miami Inst Data Sci & Comp Dept Elect & Comp Engn Coral Gables FL 33146 USA Nanyang Technol Univ NTU Sch Comp Sci & Engn SCSE Singapore City 639798 Singapore

In this paper, a semantic communication framework for image data transmission is developed. In the investigated framework, a set of servers cooperatively transmit image data to a set of users utilizing semantic communication techniques, which enable servers to transmit only the semantic information that accurately captures the meaning of images. To evaluate the performance of studied semantic communication system, a multimodal metric called image-to-graph semantic similarity (ISS) is proposed to measure the correlation between the extracted semantic information and the original image. To meet the ISS requirement of each user, each server must jointly determine the semantic information to be transmitted and the resource blocks (RBs) used for semantic information transmission. Due to the co-channel interference among users associated with different servers, each server must cooperate with other servers to find a globally optimal semantic oriented RB allocation. We formulate this problem as an optimization problem whose goal is to minimize the sum of the average transmission latency of each server while reaching the ISS requirement. To solve this problem, we propose a value decomposition based entropy-maximized multi-agent reinforcement learning (RL) algorithm. The proposed algorithm enables each server to coordinate with other servers in training stage and execute RB allocation in a distributed manner to approach to a globally optimal performance with less training iterations. Compared to traditional multi-agent RL algorithms, the proposed RL framework improves the exploration of valuable action of servers and the probability of finding a globally optimal RB allocation policy based on local observation of wireless and semantic communication environments. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 16.1% and improve the convergence speed by up to 100% compared to the traditional multi-agent RL algorithms.

关键词： Semantics Servers Measurement Resource management Communication systems Optimization image coding Semantic communications resource management reinforcement learning (RL) actor-critic semantic similarity

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DNA Encoding-Based Nucleotide Pattern and Deep Features for Instance and Class-Based image Retrieval

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2024年第1期23卷 190-201页

作者： Pradhan, Jitesh Pal, Arup Kumar Islam, S. K. Hafizul Bhaya, Chiranjeev Natl Inst Technol Jamshedpur Dept Comp Sci & Engn Jamshedpur 831014 Jharkhand India Indian Inst Technol IIT ISM Dhanbad Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India Indian Inst Informat Technol Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India

Recently, DNA encoding has shown its potential to store the vital information of the image in the form of nucleotides, namely ${A}, {C}, {T}$ , and ${G}$ , with the entire sequence following run-length and GC-constraint. As a result, the encoded DNA planes contain unique nucleotide strings, giving more salient image information using less storage. In this paper, the advantages of DNA encoding have been inherited to uplift the retrieval accuracy of the content-based image retrieval (CBIR) system. Initially, the most significant bit-plane-based DNA encoding scheme has been suggested to generate DNA planes from a given image. The generated DNA planes of the image efficiently capture the salient visual information in a compact form. Subsequently, the encoded DNA planes have been utilized for nucleotide patterns-based feature extraction and image retrieval. Simultaneously, the translated and amplified encoded DNA planes have also been deployed on different deep learning architectures like ResNet-50, VGG-16, VGG-19, and Inception V3 to perform classification-based image retrieval. The performance of the proposed system has been evaluated using two corals, an object, and a medical image dataset. All these datasets contain 28,200 images belonging to 134 different classes. The experimental results confirm that the proposed scheme achieves perceptible improvements compared with other state-of-the-art methods.

关键词： DNA Feature extraction image retrieval image coding Encoding Semantics Visualization Content-based image retrieval deep learning DNA encoding DNA nucleotides feature extraction

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VLSI IMPLEMENTATION OF INVERSE DISCRETE COSINE TRANSFORMER AND MOTION COMPENSATOR FOR MPEG2 HDTV VIDEO DEcoding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1995年第5期5卷 387-395页

作者： MASAKI, T MORIMOTO, Y ONOYE, T SHIRAKAWA, I Department of Information Systems Engineering Faculty of Engineering Osaka University Suita Osaka Japan

An MPEG2 video decoder core dedicated to MP@HL (Main Profile at High Level) images is described with the main theme focused on an inverse discrete cosine transformer and a motion compensator. By means of various novel architectures, the inverse discrete cosine transformer achieves a high throughput, and the motion compensator performs different types of picture prediction modes employed by the MPEG2 algorithm. The decoder core, implemented in the total chip area of 22.0 mm(2) by a 0.6-mu m triple-metal CMOS technology, processes a macroblock within 3.84 mu s, and therefore is capable of decoding HDTV (1920 x 1152 pels) images in real time.

关键词： Very large scale integration HDTV Decoding Transformer cores CMOS technology Asynchronous transfer mode image storage image coding Videoconference Video compression

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An Ultra-Low Complexity and High Efficiency Approach for Lossless Alpha Channel coding

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IEEE TRANSACTIONS ON MULTIMEDIA 2020年第3期22卷 786-794页

作者： Zhao, Liping Lin, Tao Zhang, Dongyu Zhou, Kailun Wang, Shuhui Shaoxing Univ Dept Comp Sci & Engn Shaoxing 312000 Peoples R China Tongji Univ Inst VLSI Coll Elect & Informat Engn Shanghai 200092 Peoples R China

Alpha channel is being applied in an increasing number of mobile web applications on mobile devices that require ultra-low power consumption in all cases including compute-intensive video encoding and decoding. Thus, we propose an ultra-low coding complexity and high efficiency alpha channel lossless coding approach. A novel coding framework and four new coding schemes are proposed for alpha channel coding. The framework fuses a string matching technique and a proposed prediction coding scheme named bit-depth preserving prediction (BDPP) together to reduce the correlations within and between repeated identical patterns and neighboring pixels. To achieve a good tradeoff between complexity and efficiency, either the unmatchable bytes are coded directly or the BDPP residuals of unmatchable bytes are coded by a proposed bytewise entropy coding scheme named 0.5-1-2byte-size-code. The other string matching parameters are coded by another proposed bytewise entropy coding scheme named byte-size multi-variable-length-code. To speed up the string-matching search, we apply a fast string search scheme that combines special position search and hash-based search. For the selected typical 236 alpha test images, compared with x265 in the fastest configuration and lossless mode, the proposed lossless approach achieves 14.33% less total compressed bytes with only 2.75% encoding and 1.83% decoding runtime. The proposed approach also outperforms the conventional lossless coding techniques such as LZ4HC, ZLIB, and PNG.

关键词： Complexity theory Channel coding image coding Standards Video coding Unified modeling language image coding alpha channel coding string matching prediction coding variable length code

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I2C: Invertible Continuous Codec for High-Fidelity Variable-Rate image Compression

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第6期46卷 4262-4279页

作者： Cai, Shilv Chen, Liqun Zhang, Zhijun Zhao, Xiangyun Zhou, Jiahuan Peng, Yuxin Yan, Luxin Zhong, Sheng Zou, Xu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Multispectral Informat Intelligent Pr Wuhan 430074 Hubei Peoples R China Waymo Mountain View CA 94043 USA Peking Univ Wangxuan Inst Comp Technol Beijing 100871 Peoples R China

Lossy image compression is a fundamental technology in media transmission and storage. Variable-rate approaches have recently gained much attention to avoid the usage of a set of different models for compressing images at different rates. During the media sharing, multiple re-encodings with different rates would be inevitably executed. However, existing Variational Autoencoder (VAE)-based approaches would be readily corrupted in such circumstances, resulting in the occurrence of strong artifacts and the destruction of image fidelity. Based on the theoretical findings of preserving image fidelity via invertible transformation, we aim to tackle the issue of high-fidelity fine variable-rate image compression and thus propose the Invertible Continuous Codec (I2C). We implement the I2C in a mathematical invertible manner with the core Invertible Activation Transformation (IAT) module. I2C is constructed upon a single-rate Invertible Neural Network (INN) based model and the quality level (QLevel) would be fed into the IAT to generate scaling and bias tensors. Extensive experiments demonstrate that the proposed I2C method outperforms state-of-the-art variable-rate image compression methods by a large margin, especially after multiple continuous re-encodings with different rates, while having the ability to obtain a very fine variable-rate control without any performance compromise.

关键词： image coding Neural networks Adaptation models Codecs Training Rate-distortion image reconstruction image processing rate-distortion

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A multimedia video conference system: Using region base hybrid coding

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1996年第3期42卷 781-786页

作者： Chen, HT Wu, PC Lai, YK Chen, LG National Taiwan University Taipei TW

In this paper, a video coding algorithm suitable for video conference application, and an investigation of a video conference system over LAN are presented. The proposed video coding algorithm is called region base hybrid coding algorithm. This method can perform foreground and background segmentation. This is based on the characteristic of an unchanged background on most of the video conference applications. This property can reduce both data rate and computation load. The proposed algorithm is verified on a video conference system over LAN, and it can work successfully.

关键词： Multimedia systems Videoconference image coding Local area networks Video compression Video coding Cameras Communication system control Transform coding Application specific integrated circuits

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SPIHT image compression on FPGAs

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2005年第9期15卷 1138-1147页

作者： Fry, TW Hauck, SA Univ Michigan Ross Sch Business Ann Arbor MI USA IBM Microelect Ridgefield CT 06877 USA Univ Washington Dept Elect Engn Seattle WA 98195 USA

In this paper, we present an implementation of the image compression routine set partitioning in hierarchical trees (SPIHT) in reconfigurable logic. A discussion on why adaptive logic is required, as opposed to an application specific integrated circuit (ASIC), is provided along with background material on the image compression algorithm. We analyzed several discrete wavelet transform (DWT) architectures and selected the folded DWT design. In addition we provide a study on what storage elements are required for each wavelet coefficient. A modification to the original SPIHT algorithm is implemented to parallelize the computation. The architecture of our SPIHT engine is based upon fixed-order SPIHT, developed specifically for use within adaptive hardware. For an N x N image fixed-order SPIHT may be calculated in N-2/4 cycles. Square images which are powers of 2 up to 1024 x 1024 are supported by the architecture. Our system was developed on an Annapolis Microsystems WildStar board populated with Xilinx Virtex-E parts. The system achieves a 450 x speedup versus a microprocessor solution, with less than a 0.2-db loss in peak signal-to-noise ratio over traditional SPIHT.

关键词： field programmable gate arrays (FPGAs) image coding reconfigurable architectures SPIHT

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Sparse coding-Enabled Low-Fluence Multi-Parametric Photoacoustic Microscopy

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第4期41卷 805-814页

作者： Wang, Zhuoying Zhou, Yifeng Hu, Song Washington Univ Dept Biomed Engn McKelvey Sch Engn St Louis MO 63130 USA

Uniquely capable of simultaneous imaging of the hemoglobin concentration, blood oxygenation, and flow speed at the microvascular level in vivo, multi-parametric photoacoustic microscopy (PAM) has shown considerable impact in biomedicine. However, the multi-parametric PAM acquisition requires dense sampling and thus a high laser pulse repetition rate (up to MHz), which sets a strict limit on the applicable pulse energy due to safety considerations. A similar limitation is shared by high-speed PAM, which also uses lasers with high pulse repetition rates. To achieve high quantitative accuracy besides good structural visualization at low levels of laser fluence in PAM, we have developed a new, sparse coding-based two-step denoising technique. In the setting of intravital brain imaging, we demonstrated that this unsupervised learning approach enabled the reduction of the laser fluence in PAM by 5 times without compromise of the image quality (structural similarity index measure or SSIM: >0.92) and the quantitative accuracy (errors: <4.9%). Offering a significant relaxation in the requirement of PAM on laser fluence while maintaining the quality of structural imaging and accuracy of quantitative measurements, this sparse coding-based approach is expected to facilitate the application and clinical translation of multi-parametric PAM and high-speed PAM, which have a tight photon budget due to either safety considerations or laser source limitations.

关键词： Noise reduction Dictionaries Laser noise Laser beams Sparse matrices Measurement by laser beam image coding Photoacoustic microscopy low-fluence functional imaging quantitative imaging sparse coding denoising

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Compressed Domain Deep Video Super-Resolution

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IEEE TRANSACTIONS ON image PROCESSING 2021年 30卷 7156-7169页

作者： Chen, Peilin Yang, Wenhan Wang, Meng Sun, Long Hu, Kangkang Wang, Shiqi City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Huawei Fields Lab Hangzhou 310000 Peoples R China

Real-world video processing algorithms are often faced with the great challenges of processing the compressed videos instead of pristine videos. Despite the tremendous successes achieved in deep-learning based video super-resolution (SR), much less work has been dedicated to the SR of compressed videos. Herein, we propose a novel approach for compressed domain deep video SR by jointly leveraging the coding priors and deep priors. By exploiting the diverse and ready-made spatial and temporal coding priors (e.g., partition maps and motion vectors) extracted directly from the video bitstream in an effortless way, the video SR in the compressed domain allows us to accurately reconstruct the high resolution video with high flexibility and substantially economized computational complexity. More specifically, to incorporate the spatial coding prior, the Guided Spatial Feature Transform (GSFT) layer is proposed to modulate features of the prior with the guidance of the video information, making the prior features more fine-grained and content-adaptive. To incorporate the temporal coding prior, a guided soft alignment scheme is designed to generate local attention off-sets to compensate for decoded motion vectors. Our soft alignment scheme combines the merits of explicit and implicit motion modeling methods, rendering the alignment of features more effective for SR in terms of the computational complexity and robustness to inaccurate motion fields. Furthermore, to fully make use of the deep priors, the multi-scale fused features are generated from a scale-wise convolution reconstruction network for final SR video reconstruction. To promote the compressed domain video SR research, we build an extensive Compressed Videos with coding Prior (CVCP) dataset, including compressed videos of diverse content and various coding priors extracted from the bitstream. Extensive experimental results show the effectiveness of coding priors in compressed domain video SR.

关键词： image coding Encoding Decoding Convolutional neural networks Superresolution Computational modeling Video coding Video compression super-resolution deep learning coding prior soft alignment

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ENcoding OF ARBITRARY CURVES BASED ON THE CHAIN CODE REPRESENTATION

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IEEE TRANSACTIONS ON COMMUNICATIONS 1985年第7期33卷 697-707页

作者： KANEKO, T OKUDAIRA, M Visual Communication Applications Section Yokosuka Electrical Communication Laboratory Nippon Telegraph and Telephone Public Corporation Kanagawa Japan

An efficient encoding scheme for arbitrary curves, based on the chain code representation, has been proposed. The encoding scheme takes advantage of the property that a curve with gentle curvature is divided into somewhat long curve sections (segments), each of which is represented by a sequence of two adjacent-direction chain codes. The coding efficiency of the scheme becomes higher as the segments become longer, while a variable-length coding technique makes it possible to encode short segments without an extreme increase in code amount. An experiment with complicated curves obtained from geographic maps has shown a high data compression rate of the proposed scheme, whose code amount is about 50-60 percent of that required for the conventional chain encoding scheme.

关键词： Encoding image coding Data compression Data communication Database systems Grid computing Clocks Joining processes Signal Processing Society

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