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Flexible Coding Order for Learned image Compression

Flexible Coding Order for Learned Image Compression

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Li, Yuqi Zhang, Haotian Liu, Dong University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9798350359855

Learned image compression (LIC) methods have made significant advances in recent years. In LIC, entropy model is an essential component, which utilizes conditional information to predict the probability distribution over the latent space. In the entropy models, many context models follow a spatially autoregressive paradigm, which leads to sequential coding. The autoregressive coding order, however, may be neither optimal nor efficient. We conduct an elaborate study on coding orders in LIC entropy models, shedding light on the potential for improving compression performance by adapting the coding order. We present Mask Modeling Context Model (MMCM), a transformer-based context model designed with a Patch-restricted Iterative Mask Modeling (PIMM) training strategy. Through training to predict the probability distribution of randomly masked tokens, we are able to use a patch-level arbitrary coding order to encode/decode the latent space in a few iterative steps. Additionally, we employ offline and online rate-distortion optimization to adaptively select the appropriate coding order for each image. Our extensive experimental results demonstrate that the proposed method achieves a better rate-distortion performance than the autoregressive context model while requiring less compression complexity. © 2023 ieee.

关键词： image compression

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REVOLUTIONIZING THERMAL IMAGING: GAN-BASED VISION TRANSFORMERS FOR image ENHANCEMENT 30

REVOLUTIONIZING THERMAL IMAGING: GAN-BASED VISION TRANSFORME...

引用

30th ieee International conference on image processing (ICIP)

作者： Marnissi, Mohamed Amine Univ Sfax Ecole Natl Ingn Sfax LATIS Lab Adv Technol & Intelligent Syst Sousse 4023 Tunisia

ISBN: (纸本)9781728198354

In this paper, we propose a new architecture for thermal image enhancement in which we exploit the strengths of both architecture-based vision transformers and generative adversarial networks. Our approach includes the introduction of a thermal loss function, which is specifically employed to produce high quality images. In addition, we consider fine-tuning based on visible images for thermal image restoration, resulting in an overall improvement in image quality. The performance of our proposed architecture is evaluated using visual quality metrics. The results show significant improvements over the original thermal images and over other established enhancement methods on a subset of the KAIST dataset. The performance of the proposed enhancement architecture is also verified on the detection results by obtaining better performance with a considerable margin considering different versions of the YOLO detector.

关键词： image enhancement generative adversarial networks vision transformers object detection

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BLIND OMNIDIRECTIONAL image QUALITY ASSESSMENT: INTEGRATING LOCAL STATISTICS AND GLOBAL SEMANTICS 30

BLIND OMNIDIRECTIONAL IMAGE QUALITY ASSESSMENT: INTEGRATING ...

引用

30th ieee International conference on image processing (ICIP)

作者： Zhou, Wei Wang, Zhou Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9781728198354

Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180x360 degrees viewing range of the visual environment. Here we propose a blind/no-reference OIQA method named Local Statistics and Global Semantics metric (LSGS) that bridges the gap between low-level statistics and high-level semantics of omnidirectional images. Specifically, statistic and semantic features are extracted in separate paths from multiple local viewports and the hallucinated global omnidirectional image, respectively. A quality regression along with a weighting process is then followed that maps the extracted quality-aware features to a perceptual quality prediction. Experimental results demonstrate that the proposed LSGS method offers highly competitive performance against state-of-the-art methods.

关键词： Omnidirectional image blind image quality assessment low-level statistics high-level semantics

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SUPPLEMENTING MISSING VISIONS VIA DIALOG FOR SCENE GRAPH GENERATIONS 49

SUPPLEMENTING MISSING VISIONS VIA DIALOG FOR SCENE GRAPH GEN...

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Zhao, Zhenghao Zhu, Ye Zhu, Xiaoguang Shang, Yuzhang Yan, Yan IIT Chicago IL 60616 USA Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Most AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various tasks. However, the classic task setup rarely considers the challenging, yet common practical situations where the complete visual data may be inaccessible due to various reasons (e.g., restricted view range and occlusions). To this end, we investigate a task setting with incomplete visual input data. Specifically, we exploit the Scene Graph Generation (SGG) task with various levels of visual data missingness as input. While insufficient visual input naturally leads to performance drop, we propose to supplement the missing visions via natural language dialog interactions to better accomplish the task objective. We design a model-agnostic Supplementary Interactive Dialog (SI-Dial) framework that can be jointly learned with most existing models, endowing the current AI systems with the ability of question-answer interactions in natural language. We demonstrate the feasibility of such task setting with missing visual input and the effectiveness of our proposed dialog module as the supplementary information source through extensive experiments, by achieving promising performance improvement over multiple baselines.

关键词： Multimodal Learning image scene understanding language modeling

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ATTENTION-BASED SPATIAL-FREQUENCY INFORMATION NETWORK FOR UNDERWATER SINGLE image SUPER-RESOLUTION 49

ATTENTION-BASED SPATIAL-FREQUENCY INFORMATION NETWORK FOR UN...

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Pramanick, Alik Megha, Dhruvil Sur, Arijit Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati India

ISBN: (纸本)9798350344868;9798350344851

Underwater single image super-resolution (UISR) is a challenging task as these images frequently suffer from poor visibility. The best-published UISR works continue to suffer from color degradation, poor texture representation, and loss of finer (high-frequency) details. We propose a novel deep learning-based (DL) UISR model that incorporates spatial information as well as the transformed (wavelet) coefficient of degraded low-resolution (LR) underwater images by intelligent feature management. To ensure the visual quality of the super-resolved image, color channel-specific L1 loss, perceptual loss, and difference of Gaussian (DoG) loss are used in tandem with SSIM loss. We employ publicly available datasets, namely UFO-120 and USR-248, to evaluate the proposed model. The results of our experiments show that our model outperforms existing state-of-the-art methods (e.g., similar to 9.45%/similar to 1.77% in SSIM and similar to 0.91%/similar to 1.44% in PSNR on UFO-120/USR-248 x4, respectively), as demonstrated through quantitative measurements and visual quality assessments.

关键词： Underwater image super-resolution spatial domain frequency domain deep learning

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ENHANCED DETECTION OF SMALL OBJECTS IN AERIAL imageRY: A HIGH-RESOLUTION NEURAL NETWORK APPROACH WITH AMPLIFIED FEATURE PYRAMID AND SIGMOID RE-WEIGHTING 31

ENHANCED DETECTION OF SMALL OBJECTS IN AERIAL IMAGERY: A HIG...

引用

2024 International conference on image processing

作者： Park, Chanyeong Jang, Junbo Kim, Heegwang Paik, Joonki Chung Ang Univ Dept Image Seoul South Korea Chung Ang Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (纸本)9798350349405;9798350349399

Detecting small objects in drone-captured images or aerial videos is challenging due to their minimal representation. As data traverses deep learning networks, the information about small objects can diminish, making high-resolution images essential for enhanced detection performance. However, high-resolution images increase computational load undesirably. Leveraging this fact, we propose a streamlined neural network designed specifically for small object detection in high-resolution images. The proposed network encompasses three main components: i) Enhanced High-Resolution processing Module (EHRPM), ii) the Small Object Feature Amplified Feature Pyramid Network (SOFA-FPN) with its Edge Enhancement Module (EEM), Cross Lateral Connection Module (CLCM), and Dual Bottom-up Convolution Module (DBCM), and iii) the Sigmoid Re-weighting Module (SRM). Compared to several state-of-the-art networks, our method delivers superior performance with fewer parameters and a lower computational demand. The source code is available at https://***/datu0615/EHRPM.

关键词： Small Object Detection Aerial images Multi-scale Feature Fusion High-resolution processing

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COMMIN: SEMANTIC image communications AS AN INVERSE PROBLEM WITH INN-GUIDED DIFFUSION MODELS 49

COMMIN: SEMANTIC IMAGE COMMUNICATIONS AS AN INVERSE PROBLEM ...

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Chen, Jiakang You, Di Gunduz, Deniz Dragotti, Pier Luigi Imperial Coll London Dept Elect & Elect Engn London England

ISBN: (纸本)9798350344868;9798350344851

Joint source-channel coding schemes based on deep neural networks (DeepJSCC) have recently achieved remarkable performance for wireless image transmission. However, these methods usually focus only on the distortion of the reconstructed signal at the receiver side with respect to the source at the transmitter side, rather than the perceptual quality of the reconstruction which carries more semantic information. As a result, severe perceptual distortion can be introduced under extreme conditions such as low bandwidth and low signal-to-noise ratio. In this work, we propose CommIN, which views the recovery of high-quality source images from degraded reconstructions as an inverse problem. To address this, CommIN combines Invertible Neural Networks (INN) with diffusion models, aiming for superior perceptual quality. Through experiments, we show that our CommIN significantly improves the perceptual quality compared to DeepJSCC under extreme conditions and outperforms other inverse problem approaches used in DeepJSCC.

关键词： Semantic communications joint source-channel coding inverse problems invertible neural networks diffusion models

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Learned image Compression with Variable Neural Network Architecture

Learned Image Compression with Variable Neural Network Archi...

引用

2023 ieee International conference on visual communications and image processing, VCIP 2023

作者： Zeng, Ziyi Yu, Lu Zhejiang University Hangzhou China

ISBN: (纸本)9798350359855

With the development of neural networks, the coding efficiency of learned image compression methods gradually exceeds that of traditional image codecs that are carefully designed and optimized by experts. However, the deep image compression model with an invariable network structure and fixed network parameters can only cover a limited range of bit rates. Using the same network structure for different bit rate ranges leads to sub-optimal coding efficiency. Meanwhile, the more complex network structure and the increase of network parameters cause higher model complexity, which makes it difficult for network training and practical application. In this paper, a new perspective is proposed to solve the above challenges. The idea of variable network architecture is introduced to the field of learned image compression. We devise two strategies with variable network architecture, which improve the coding performance of the learned image compression model from the coding efficiency and complexity respectively. Compared to the image compression method of fixed network structure, our first approach attains an average BDPSNR of 0.771dB and an average BDBR of-14.876%. The second attains an average BDPSNR of 0.320dB and an average BDBR of-5.471%. These results demonstrate the effectiveness of variable network structure in improving coding efficiency and controlling model complexity. © 2023 ieee.

关键词： Network architecture

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COMPOUND MULTI-BRANCH FEATURE FUSION FOR image DERAINDROP 30

COMPOUND MULTI-BRANCH FEATURE FUSION FOR IMAGE DERAINDROP

引用

30th ieee International conference on image processing (ICIP)

作者： Fan, Chi-Mao Liu, Tsung-Jung Liu, Kuan-Hsien Natl Chung Hsing Univ Dept Elect Engn Taichung Taiwan Natl Chung Hsing Univ Grad Inst Commun Engn Taichung Taiwan Natl Taichung Univ Sci & Technol Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781728198354

image restoration is a challenging and ill-posed problem which also has been a long-standing issue. In this paper, we proposed a multi-branch restoration model inspired from the Human visual System (i.e., Retinal Ganglion Cells) for image deraindrop. The experiments show that the proposed multi-branch architecture, called CMFNet, has state-of-the-art performance results. The source code and pretrained models are available at https: //***/FanChiMao/CMFNet. And the interactive demonstration of the proposed deraindrop model can be accessed at https://***/dXaeNg.

关键词： image restoration deraining deraindrop branch network mixed skip connection

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L2FUSION: LOW-LIGHT ORIENTED INFRARED AND VISIBLE image FUSION 30

L2FUSION: LOW-LIGHT ORIENTED INFRARED AND VISIBLE IMAGE FUSI...

引用

30th ieee International conference on image processing (ICIP)

作者： Gao, Xiang Lv, Guohua Dong, Aimei Wei, Zhonghe Cheng, Jinyong Qilu Univ Technol Shandong Acad Sci Minist EducNatl Supercomp Ctr JinanShandong Com Key Lab Comp Power Network & Informat Secur Jinan Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan Peoples R China

ISBN: (纸本)9781728198354

Infrared and visible image fusion aims to integrate salient targets and abundant texture information into a single fused image. Existing methods typically ignore the issue of illumination, so that there are problems of weak texture details and poor visual perception in case of low illumination. To address this issue, we propose a low-light oriented infrared and visible image fusion network, named L2Fusion. In particular, we first design a decomposition network according to Retinex theory to obtain the reflectance features of a visible image with low-light. Then, these features are integrated with the features extracted from the corresponding infrared image by a residual network. The finally fused image largely eliminates the negative impact caused by low illumination, and contains both salient targets and abundant texture information. Extensive experiments demonstrate the superiority of our L2Fusion over the state-of-the-art methods, in terms of both visual effect and quantitative metrics.

关键词： image fusion low-light deep learning infrared image visible image

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