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Learned Structure-based Hybrid Framework for Martian image compression

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Li, Shengxi Sun, Xiancheng Xu, Mai Jiang, Lai Zou, Xin Beihang Univ Dept Elect & Informat Engn Beijing 100191 Peoples R China Beijing Inst Spacecraft Syst Engn Beijing 100094 Peoples R China

Recent landing marches on Mars have enabled the access to Martian surface images, which act as an important vehicle to demystify the evolution and habitability of Mars, in terms of climate, geography, etc. Transmitting Martian images thus calls for efficient compression methods to ensure the high-quality reconstruction from distant communication, in which the research is yet to start. To address this issue, we propose in this letter a learned structure-based hybrid (LSH) framework to compress Martian images. More specifically, we first observe that the structural consistency exists across Martian images, which motivates us to propose a structural compression network (SCN). The aim of SCN is to compactly represent the structural information of Martian images, thus allowing for the compression at extremely low bit-rates. Then, we propose a detail compensation network (DCN) to reconstruct the missing details when we restore from the structural information, which benefits from improved compression efficiency by reduced bit-rates. The experimental results have verified the superior performances of our LSH method on compressing Martian images, against existing state-of-the-art methods.

关键词： image coding image reconstruction Standards Transform coding image restoration Convolution Mars Deep neural network (DNN) learning-based image compression Martian image compression (MIC)

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Compressed-Domain Vision Transformer for image Classification

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IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS 2024年第2期14卷 299-310页

作者： Ji, Ruolei Karam, Lina J. Arizona State Univ Sch ECEE Image Video & Usabil Lab Tempe AZ 85287 USA

Compressed-domain visual task schemes, where visual processing or computer vision are directly performed on the compressed-domain representations, were shown to achieve a higher computational efficiency during training and deployment by avoiding the need to decode the compressed visual information while resulting in a competitive or even better performance as compared to corresponding spatial-domain visual tasks. This work is concerned with learning-based compressed-domain image classification, where the image classification is performed directly on compressed-domain representations, also known as latent representations, that are obtained using a learning-based visual encoder. In this paper, a compressed-domain Vision Transformer (cViT) is proposed to perform image classification in the learning-based compressed-domain. For this purpose, the Vision Transformer (ViT) architecture is adopted and modified to perform classification directly in the compressed-domain. As part of this work, a novel feature patch embedding is introduced leveraging the within- and cross-channel information in the compressed-domain. Also, an adaptation training strategy is designed to adopt the weights from the pre-trained spatial-domain ViT and adapt these to the compressed-domain classification task. Furthermore, the pre-trained ViT weights are utilized through interpolation for position embedding initialization to further improve the performance of cViT. The experimental results show that the proposed cViT outperforms the existing compressed-domain classification networks in terms of Top-1 and Top-5 classification accuracies. Moreover, the proposed cViT can yield competitive classification accuracies with a significantly higher computational efficiency as compared to pixel-domain approaches.

关键词： image coding Transformers Visualization Task analysis Entropy Transforms image reconstruction Transformer deep learning compressed-domain classification learning-based image compression image classification

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Designing Deep Neural High-Density compression Engines for Radiology images

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第2期42卷 643-682页

作者： Raj, Aditya Sathish, Rakshith Sarkar, Tandra Sethuraman, Ramanathan Sheet, Debdoot Indian Inst Technol Kharagpur Kharagpur W Bengal India Apollo Gleneagles Hosp Kolkata W Bengal India Intel Technol India Pvt Ltd Bangalore Karnataka India

As a speciality, radiology produces the highest volume of medical images in clinical establishments compared to other commonly employed imaging modalities like digital pathology, ophthalmic imaging, etc. Archiving this massive quantity of images with large file sizes is a major problem since the costs associated with storing medical images continue to rise with an increase in cost of electronic storage devices. One of the possible solutions is to compress them for effective storage. The prime challenge is that each modality is distinctively characterized by dynamic range and resolution of the signal and its spatial and statistical distribution. Such variations in medical images are different from camera-acquired natural scene images. Thus, conventional natural image compression algorithms such as J2K and JPEG often fail to preserve the clinically relevant details present in medical images. We address this challenge by developing a modality-specific compressor and a modality-agnostic generic decompressor implemented using a deep neural network (DNN) and capable of preserving clinically relevant image information. Architecture of the DNN is obtained through design space exploration (DSE) with the objective to feature the least computational complexity at the highest compression and a target high-quality factor, thereby leading to a low power requirement for computation. The neural compressed bitstream is further compressed using the lossless Huffman encoding to obtain a variable bit length and high-density compression (20 x -400x). Experimental validation is performed on X-ray, CT and MRI. Through quantitative measurement and clinical validation with a radiologist in the loop, we experimentally demonstrate our approach's performance superiority over traditional methods like JPEG and J2K operating at matching compression factors.

关键词： learning-based image compression High-density radiology image compression Neural architecture search Design space exploration

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Towards super resolution in the compressed domain of learning-based image codecs 44

Towards super resolution in the compressed domain of learnin...

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Conference Applications of Digital image Processing XLIV

作者： Upenik, Evgeniy Testolina, Michela Ebrahimi, Touradj Ecole Polytech Fed Lausanne EPFL Multimedia Signal Proc Grp MMSPG CH-1015 Lausanne Switzerland

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

learning-based image coding has shown promising results during recent years. Unlike the traditional approaches to image compression, learning-based codecs exploit deep neural networks for reducing dimensionality of the input at the stage where a linear transform would be typically applied previously. The signal representation after this stage, called latent space, carries the information in such a way that it can be interpreted by other deep neural networks without the need of decoding it. One of the tasks that can benefit from the above-mentioned possibility is super resolution. In this paper, we explore the possibilities and propose an approach for super resolution that is applied in the latent space. We focus on the fixed compression model, where the encoder part of the network is frozen and an enhanced decoder is learned. Additionally, we assess the performance of the proposed approach.

关键词： image processing super resolution learning-based image compression deep learning

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Perceptual impact of the loss function on deep-learning image coding performance

Perceptual impact of the loss function on deep-learning imag...

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Picture Coding Symposium (PCS)

作者： Mohammadi, Shima Ascenso, Joao Inst Super Tecn Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9781665492577

Nowadays, deep-learning image coding solutions have shown similar or better compression efficiency than conventional solutions based on hand-crafted transforms and spatial prediction techniques. These deep-learning codecs require a large training set of images and a training methodology to obtain a suitable model (set of parameters) for efficient compression. The training is performed with an optimization algorithm which provides a way to minimize the loss function. Therefore, the loss function plays a key role in the overall performance and includes a differentiable quality metric that attempts to mimic human perception. The main objective of this paper is to study the perceptual impact of several image quality metrics that can be used in the loss function of the training process, through a crowdsourcing subjective image quality assessment study. From this study, it is possible to conclude that the choice of the quality metric is critical for the perceptual performance of the deep-learning codec and that can vary depending on the image content.

关键词： learning-based image compression percepual pptimization loss function image quality metrics

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Learn A compression for Objection Detection - VAE with a Bridge

Learn A Compression for Objection Detection - VAE with a Bri...

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IEEE International Conference on Visual Communications and image Processing (VCIP) - Visual Communications in the Era of AI and Limited Resources

作者： Mei, Yixin Li, Fan Li, Li Li, Zhu Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei Peoples R China Univ Missouri Dept Comp Sci & Elect Engn Kansas City MO 64110 USA

ISBN: (纸本)9781728185514

Recent advances in sensor technology and wide deployment of visual sensors lead to a new application whereas compression of images are not mainly for pixel recovery for human consumption, instead it is for communication to cloud side machine vision tasks like classification, identification, detection and tracking. This opens up new research dimensions for a learning based compression that directly optimizes loss function in vision tasks, and therefore achieves better compression performance vis-a-vis the pixel recovery and then performing vision tasks computing. In this work, we developed a learning based compression scheme that learns a compact feature representation and appropriate bitstreams for the task of visual object detection. Variational Auto-Encoder (VAE) framework is adopted for learning a compact representation, while a bridge network is trained to drive the detection loss function. Simulation results demonstrate that this approach is achieving a new state-of-the-art in task driven compression efficiency, compared with pixel recovery approaches, including both learning based and handcrafted solutions.

关键词： image coding for machine object detection learning-based image compression

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COMPLEXITY SCALABLE learning-based image DECODING 30

COMPLEXITY SCALABLE LEARNING-BASED IMAGE DECODING

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30th IEEE International Conference on image Processing (ICIP)

作者： Munna, Tahsir Ahmed Ascenso, Joao Inst Telecomunicacoes Inst Super Tecn Lisbon Portugal

ISBN: (纸本)9781728198354

Recently, learning-based image compression has attracted a lot of attention, leading to the development of a new JPEG AI standard based on neural networks. Typically, this type of coding solution has much lower encoding complexity compared to conventional coding standards such as HEVC and VVC (Intra mode) but has much higher decoding complexity. Therefore, to promote the wide adoption of learning-based image compression, especially to resource-constrained (such as mobile) devices, it is important to achieve lower decoding complexity even if at the cost of some coding efficiency. This paper proposes a complexity scalable decoder that can control the decoding complexity by proposing a novel procedure to learn the filters of the convolutional layers at the decoder by varying the number of channels at each layer, effectively having simple to more complex decoding networks. A regularization loss is employed with pruning after training to obtain a set of scalable layers, which may use more or fewer channels depending on the complexity budget. Experimental results show that complexity can be significantly reduced while still allowing a competitive rate-distortion performance.

关键词： learning-based image compression complexity scalability hyperprior neural networks

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A no-reference perceptual image quality assessment database for learned image codecs

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JOURNAL OF VISUAL COMMUNICATION AND image REPRESENTATION 2022年 88卷

作者： Zhang, Jiaqi Fang, Zhigao Yu, Lu Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou Peoples R China

The drastic growth of research in image compression, especially deep learning-based image compression techniques, poses new challenges to objective image quality assessment (IQA). Typical artifacts encountered in the emerging image codecs are significantly different from that produced by traditional block-based codecs, leading to inapplicability of the existing objective IQA algorithms. Towards advancing the development of objective IQA algorithms for recent compression artifacts, we built a learning-based compressed image quality assessment (LCIQA) database involving traditional block-based image codecs, hybrid neural network based image codecs, convolutional neural network based and generative adversarial network (GAN) based end-to-end optimized image coding approaches. Our study confirms the statistical difference and human perception difference between reconstructions of learned compression and traditional block-based compression. We propose a two-step deep learning model for learning-based compressed image quality assessment. Extensive experiments on LCIQA database demonstrate that our proposed model performs better than other counterparts on learning-based compressed images, especially on GAN compressed images, and achieves competitive performance to the state-of-the-art IQA metrics on traditional compressed images.

关键词： image quality assessment learning-based image compression Generated image compression

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