Pub Date : 2025-01-06DOI: 10.1109/TIP.2024.3523802
Qi Bi;Beichen Zhou;Wei Ji;Gui-Song Xia
Existing fine-grained visual categorization (FGVC) methods assume that the fine-grained semantics rest in the informative parts of an image. This assumption works well on favorable front-view object-centric images, but can face great challenges in many real-world scenarios, such as scene-centric images (e.g., street view) and adverse viewpoint (e.g., object re-identification, remote sensing). In such scenarios, the mis-/over- feature activation is likely to confuse the part selection and degrade the fine-grained representation. In this paper, we are motivated to design a universal FGVC framework for real-world scenarios. More precisely, we propose a concept guided learning (CGL), which models concepts of a certain fine-grained category as a combination of inherited concepts from its subordinate coarse-grained category and discriminative concepts from its own. The discriminative concepts is utilized to guide the fine-grained representation learning. Specifically, three key steps are designed, namely, concept mining, concept fusion, and concept constraint. On the other hand, to bridge the FGVC dataset gap under scene-centric and adverse viewpoint scenarios, a Fine-grained Land-cover Categorization Dataset (FGLCD) with 59,994 fine-grained samples is proposed. Extensive experiments show the proposed CGL: 1) has a competitive performance on conventional FGVC; 2) achieves state-of-the-art performance on fine-grained aerial scenes & scene-centric street scenes; 3) good generalization on object re-identification and fine-grained aerial object detection. The dataset and source code will be available at https://github.com/BiQiWHU/CGL.
{"title":"Universal Fine-Grained Visual Categorization by Concept Guided Learning","authors":"Qi Bi;Beichen Zhou;Wei Ji;Gui-Song Xia","doi":"10.1109/TIP.2024.3523802","DOIUrl":"10.1109/TIP.2024.3523802","url":null,"abstract":"Existing fine-grained visual categorization (FGVC) methods assume that the fine-grained semantics rest in the informative parts of an image. This assumption works well on favorable front-view object-centric images, but can face great challenges in many real-world scenarios, such as scene-centric images (e.g., street view) and adverse viewpoint (e.g., object re-identification, remote sensing). In such scenarios, the mis-/over- feature activation is likely to confuse the part selection and degrade the fine-grained representation. In this paper, we are motivated to design a universal FGVC framework for real-world scenarios. More precisely, we propose a concept guided learning (CGL), which models concepts of a certain fine-grained category as a combination of inherited concepts from its subordinate coarse-grained category and discriminative concepts from its own. The discriminative concepts is utilized to guide the fine-grained representation learning. Specifically, three key steps are designed, namely, concept mining, concept fusion, and concept constraint. On the other hand, to bridge the FGVC dataset gap under scene-centric and adverse viewpoint scenarios, a Fine-grained Land-cover Categorization Dataset (FGLCD) with 59,994 fine-grained samples is proposed. Extensive experiments show the proposed CGL: 1) has a competitive performance on conventional FGVC; 2) achieves state-of-the-art performance on fine-grained aerial scenes & scene-centric street scenes; 3) good generalization on object re-identification and fine-grained aerial object detection. The dataset and source code will be available at <uri>https://github.com/BiQiWHU/CGL</uri>.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"394-409"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-06DOI: 10.1109/TIP.2024.3523798
Rongrong Wang;Yuhu Cheng;Xuesong Wang
Safe reinforcement learning aims to ensure the optimal performance while minimizing potential risks. In real-world applications, especially in scenarios that rely on visual inputs, a key challenge lies in the extraction of essential features for safe decision-making while maintaining the sample efficiency. To address this issue, we propose the constrained visual representation learning with bisimulation metrics for safe reinforcement learning (CVRL-BM). CVRL-BM constructs a sequential conditional variational inference model to compress high-dimensional visual observations into low-dimensional state representations. Additionally, safety bisimulation metrics are introduced to quantify the behavioral similarity between states, and our objective is to make the distance between any two latent state representations as close as possible to the safety bisimulation metric between their corresponding states. By integrating these two components, CVRL-BM is able to learn compact and information-rich visual state representations while satisfying predefined safety constraints. Experiments on Safety Gym show that CVRL-BM outperforms existing vision-based safe reinforcement learning methods in safety and efficacy. Particularly, CVRL-BM surpasses the state-of-the-art Safe SLAC method by achieving a 19.748% higher reward return, a 41.772% lower cost return, and a 5.027% decrease in cost regret. These results highlight the effectiveness of our proposed CVRL-BM.
{"title":"Constrained Visual Representation Learning With Bisimulation Metrics for Safe Reinforcement Learning","authors":"Rongrong Wang;Yuhu Cheng;Xuesong Wang","doi":"10.1109/TIP.2024.3523798","DOIUrl":"10.1109/TIP.2024.3523798","url":null,"abstract":"Safe reinforcement learning aims to ensure the optimal performance while minimizing potential risks. In real-world applications, especially in scenarios that rely on visual inputs, a key challenge lies in the extraction of essential features for safe decision-making while maintaining the sample efficiency. To address this issue, we propose the constrained visual representation learning with bisimulation metrics for safe reinforcement learning (CVRL-BM). CVRL-BM constructs a sequential conditional variational inference model to compress high-dimensional visual observations into low-dimensional state representations. Additionally, safety bisimulation metrics are introduced to quantify the behavioral similarity between states, and our objective is to make the distance between any two latent state representations as close as possible to the safety bisimulation metric between their corresponding states. By integrating these two components, CVRL-BM is able to learn compact and information-rich visual state representations while satisfying predefined safety constraints. Experiments on Safety Gym show that CVRL-BM outperforms existing vision-based safe reinforcement learning methods in safety and efficacy. Particularly, CVRL-BM surpasses the state-of-the-art Safe SLAC method by achieving a 19.748% higher reward return, a 41.772% lower cost return, and a 5.027% decrease in cost regret. These results highlight the effectiveness of our proposed CVRL-BM.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"379-393"},"PeriodicalIF":0.0,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-01DOI: 10.1109/TIP.2024.3513592
{"title":"Reviewer Summary for Transactions on Image Processing","authors":"","doi":"10.1109/TIP.2024.3513592","DOIUrl":"10.1109/TIP.2024.3513592","url":null,"abstract":"","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"33 ","pages":"6905-6925"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10819972","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142911979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-30DOI: 10.1109/TIP.2024.3521301
Tom Bordin;Thomas Maugey
This study addresses the challenge of controlling the global color aspect of images generated by a diffusion model without training or fine-tuning. We rewrite the guidance equations to ensure that the outputs are closer to a known color map, without compromising the quality of the generation. Our method results in new guidance equations. In the context of color guidance, we show that the scaling of the guidance should not decrease but rather increase throughout the diffusion process. In a second contribution, our guidance is applied in a compression framework, where we combine both semantic and general color information of the image to decode at very low cost. We show that our method is effective in improving the fidelity and realism of compressed images at extremely low bit rates ($10^{-2}$ bpp), performing better on these criteria when compared to other classical or more semantically oriented approaches. The implementation of our method is available on gitlab at https://gitlab.inria.fr/tbordin/color-guidance.
{"title":"Linearly Transformed Color Guide for Low-Bitrate Diffusion-Based Image Compression","authors":"Tom Bordin;Thomas Maugey","doi":"10.1109/TIP.2024.3521301","DOIUrl":"10.1109/TIP.2024.3521301","url":null,"abstract":"This study addresses the challenge of controlling the global color aspect of images generated by a diffusion model without training or fine-tuning. We rewrite the guidance equations to ensure that the outputs are closer to a known color map, without compromising the quality of the generation. Our method results in new guidance equations. In the context of color guidance, we show that the scaling of the guidance should not decrease but rather increase throughout the diffusion process. In a second contribution, our guidance is applied in a compression framework, where we combine both semantic and general color information of the image to decode at very low cost. We show that our method is effective in improving the fidelity and realism of compressed images at extremely low bit rates (<inline-formula> <tex-math>$10^{-2}$ </tex-math></inline-formula>bpp), performing better on these criteria when compared to other classical or more semantically oriented approaches. The implementation of our method is available on gitlab at <uri>https://gitlab.inria.fr/tbordin/color-guidance</uri>.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"468-482"},"PeriodicalIF":0.0,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-25DOI: 10.1109/TIP.2024.3512354
Shuai Zhao;Ruijie Quan;Linchao Zhu;Yi Yang
Pre-trained vision-language models (VLMs) are the de-facto foundation models for various downstream tasks. However, scene text recognition methods still prefer backbones pre-trained on a single modality, namely, the visual modality, despite the potential of VLMs to serve as powerful scene text readers. For example, CLIP can robustly identify regular (horizontal) and irregular (rotated, curved, blurred, or occluded) text in images. With such merits, we transform CLIP into a scene text reader and introduce CLIP4STR, a simple yet effective STR method built upon image and text encoders of CLIP. It has two encoder-decoder branches: a visual branch and a cross-modal branch. The visual branch provides an initial prediction based on the visual feature, and the cross-modal branch refines this prediction by addressing the discrepancy between the visual feature and text semantics. To fully leverage the capabilities of both branches, we design a dual predict-and-refine decoding scheme for inference. We scale CLIP4STR in terms of the model size, pre-training data, and training data, achieving state-of-the-art performance on 13 STR benchmarks. Additionally, a comprehensive empirical study is provided to enhance the understanding of the adaptation of CLIP to STR. Our method establishes a simple yet strong baseline for future STR research with VLMs.
{"title":"CLIP4STR: A Simple Baseline for Scene Text Recognition With Pre-Trained Vision-Language Model","authors":"Shuai Zhao;Ruijie Quan;Linchao Zhu;Yi Yang","doi":"10.1109/TIP.2024.3512354","DOIUrl":"10.1109/TIP.2024.3512354","url":null,"abstract":"Pre-trained vision-language models (VLMs) are the de-facto foundation models for various downstream tasks. However, scene text recognition methods still prefer backbones pre-trained on a single modality, namely, the visual modality, despite the potential of VLMs to serve as powerful scene text readers. For example, CLIP can robustly identify regular (horizontal) and irregular (rotated, curved, blurred, or occluded) text in images. With such merits, we transform CLIP into a scene text reader and introduce CLIP4STR, a simple yet effective STR method built upon image and text encoders of CLIP. It has two encoder-decoder branches: a visual branch and a cross-modal branch. The visual branch provides an initial prediction based on the visual feature, and the cross-modal branch refines this prediction by addressing the discrepancy between the visual feature and text semantics. To fully leverage the capabilities of both branches, we design a dual predict-and-refine decoding scheme for inference. We scale CLIP4STR in terms of the model size, pre-training data, and training data, achieving state-of-the-art performance on 13 STR benchmarks. Additionally, a comprehensive empirical study is provided to enhance the understanding of the adaptation of CLIP to STR. Our method establishes a simple yet strong baseline for future STR research with VLMs.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"33 ","pages":"6893-6904"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1109/TIP.2024.3519997
Chao Xie;Linfeng Fei;Huanjie Tao;Yaocong Hu;Wei Zhou;Jiun Tian Hoe;Weipeng Hu;Yap-Peng Tan
Recently, neural networks have become the dominant approach to low-light image enhancement (LLIE), with at least one-third of them adopting a Retinex-related architecture. However, through in-depth analysis, we contend that this most widely accepted LLIE structure is suboptimal, particularly when addressing the non-uniform illumination commonly observed in natural images. In this paper, we present a novel variant learning framework, termed residual quotient learning, to substantially alleviate this issue. Instead of following the existing Retinex-related decomposition-enhancement-reconstruction process, our basic idea is to explicitly reformulate the light enhancement task as adaptively predicting the latent quotient with reference to the original low-light input using a residual learning fashion. By leveraging the proposed residual quotient learning, we develop a lightweight yet effective network called ResQ-Net. This network features enhanced non-uniform illumination modeling capabilities, making it more suitable for real-world LLIE tasks. Moreover, due to its well-designed structure and reference-free loss function, ResQ-Net is flexible in training as it allows for zero-reference optimization, which further enhances the generalization and adaptability of our entire framework. Extensive experiments on various benchmark datasets demonstrate the merits and effectiveness of the proposed residual quotient learning, and our trained ResQ-Net outperforms state-of-the-art methods both qualitatively and quantitatively. Furthermore, a practical application in dark face detection is explored, and the preliminary results confirm the potential and feasibility of our method in real-world scenarios.
{"title":"Residual Quotient Learning for Zero-Reference Low-Light Image Enhancement","authors":"Chao Xie;Linfeng Fei;Huanjie Tao;Yaocong Hu;Wei Zhou;Jiun Tian Hoe;Weipeng Hu;Yap-Peng Tan","doi":"10.1109/TIP.2024.3519997","DOIUrl":"10.1109/TIP.2024.3519997","url":null,"abstract":"Recently, neural networks have become the dominant approach to low-light image enhancement (LLIE), with at least one-third of them adopting a Retinex-related architecture. However, through in-depth analysis, we contend that this most widely accepted LLIE structure is suboptimal, particularly when addressing the non-uniform illumination commonly observed in natural images. In this paper, we present a novel variant learning framework, termed residual quotient learning, to substantially alleviate this issue. Instead of following the existing Retinex-related decomposition-enhancement-reconstruction process, our basic idea is to explicitly reformulate the light enhancement task as adaptively predicting the latent quotient with reference to the original low-light input using a residual learning fashion. By leveraging the proposed residual quotient learning, we develop a lightweight yet effective network called ResQ-Net. This network features enhanced non-uniform illumination modeling capabilities, making it more suitable for real-world LLIE tasks. Moreover, due to its well-designed structure and reference-free loss function, ResQ-Net is flexible in training as it allows for zero-reference optimization, which further enhances the generalization and adaptability of our entire framework. Extensive experiments on various benchmark datasets demonstrate the merits and effectiveness of the proposed residual quotient learning, and our trained ResQ-Net outperforms state-of-the-art methods both qualitatively and quantitatively. Furthermore, a practical application in dark face detection is explored, and the preliminary results confirm the potential and feasibility of our method in real-world scenarios.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"365-378"},"PeriodicalIF":0.0,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142884230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-23DOI: 10.1109/TIP.2024.3518759
Yang Yang;Wenjuan Xi;Luping Zhou;Jinhui Tang
Vision-language retrieval aims to search for similar instances in one modality based on queries from another modality. The primary objective is to learn cross-modal matching representations in a latent common space. Actually, the assumption underlying cross-modal matching is modal balance, where each modality contains sufficient information to represent the others. However, noise interference and modality insufficiency often lead to modal imbalance, making it a common phenomenon in practice. The impact of imbalance on retrieval performance remains an open question. In this paper, we first demonstrate that ultimate cross-modal matching is generally sub-optimal for cross-modal retrieval when imbalanced modalities exist. The structure of instances in the common space is inherently influenced when facing imbalanced modalities, posing a challenge to cross-modal similarity measurement. To address this issue, we emphasize the importance of meaningful structure-preserved matching. Accordingly, we propose a simple yet effective method to rebalance cross-modal matching by learning structure-preserved matching representations. Specifically, we design a novel multi-granularity cross-modal matching that incorporates structure-aware distillation alongside the cross-modal matching loss. While the cross-modal matching loss constraints instance-level matching, the structure-aware distillation further regularizes the geometric consistency between learned matching representations and intra-modal representations through the developed relational matching. Extensive experiments on different datasets affirm the superior cross-modal retrieval performance of our approach, simultaneously enhancing single-modal retrieval capabilities compared to the baseline models.
{"title":"Rebalanced Vision-Language Retrieval Considering Structure-Aware Distillation","authors":"Yang Yang;Wenjuan Xi;Luping Zhou;Jinhui Tang","doi":"10.1109/TIP.2024.3518759","DOIUrl":"10.1109/TIP.2024.3518759","url":null,"abstract":"Vision-language retrieval aims to search for similar instances in one modality based on queries from another modality. The primary objective is to learn cross-modal matching representations in a latent common space. Actually, the assumption underlying cross-modal matching is modal balance, where each modality contains sufficient information to represent the others. However, noise interference and modality insufficiency often lead to modal imbalance, making it a common phenomenon in practice. The impact of imbalance on retrieval performance remains an open question. In this paper, we first demonstrate that ultimate cross-modal matching is generally sub-optimal for cross-modal retrieval when imbalanced modalities exist. The structure of instances in the common space is inherently influenced when facing imbalanced modalities, posing a challenge to cross-modal similarity measurement. To address this issue, we emphasize the importance of meaningful structure-preserved matching. Accordingly, we propose a simple yet effective method to rebalance cross-modal matching by learning structure-preserved matching representations. Specifically, we design a novel multi-granularity cross-modal matching that incorporates structure-aware distillation alongside the cross-modal matching loss. While the cross-modal matching loss constraints instance-level matching, the structure-aware distillation further regularizes the geometric consistency between learned matching representations and intra-modal representations through the developed relational matching. Extensive experiments on different datasets affirm the superior cross-modal retrieval performance of our approach, simultaneously enhancing single-modal retrieval capabilities compared to the baseline models.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"33 ","pages":"6881-6892"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-23DOI: 10.1109/TIP.2024.3518097
Jiarui Zhang;Ruixu Geng;Xiaolong Du;Yan Chen;Houqiang Li;Yang Hu
Passive non-line-of-sight (NLOS) imaging has witnessed rapid development in recent years, due to its ability to image objects that are out of sight. The light transport condition plays an important role in this task since changing the conditions will lead to different imaging models. Existing learning-based NLOS methods usually train independent models for different light transport conditions, which is computationally inefficient and impairs the practicality of the models. In this work, we propose NLOS-LTM, a novel passive NLOS imaging method that effectively handles multiple light transport conditions with a single network. We achieve this by inferring a latent light transport representation from the projection image and using this representation to modulate the network that reconstructs the hidden image from the projection image. We train a light transport encoder together with a vector quantizer to obtain the light transport representation. To further regulate this representation, we jointly learn both the reconstruction network and the reprojection network during training. A set of light transport modulation blocks is used to modulate the two jointly trained networks in a multi-scale way. Extensive experiments on a large-scale passive NLOS dataset demonstrate the superiority of the proposed method. The code is available at https://github.com/JerryOctopus/NLOS-LTM.
{"title":"Passive Non-Line-of-Sight Imaging With Light Transport Modulation","authors":"Jiarui Zhang;Ruixu Geng;Xiaolong Du;Yan Chen;Houqiang Li;Yang Hu","doi":"10.1109/TIP.2024.3518097","DOIUrl":"10.1109/TIP.2024.3518097","url":null,"abstract":"Passive non-line-of-sight (NLOS) imaging has witnessed rapid development in recent years, due to its ability to image objects that are out of sight. The light transport condition plays an important role in this task since changing the conditions will lead to different imaging models. Existing learning-based NLOS methods usually train independent models for different light transport conditions, which is computationally inefficient and impairs the practicality of the models. In this work, we propose NLOS-LTM, a novel passive NLOS imaging method that effectively handles multiple light transport conditions with a single network. We achieve this by inferring a latent light transport representation from the projection image and using this representation to modulate the network that reconstructs the hidden image from the projection image. We train a light transport encoder together with a vector quantizer to obtain the light transport representation. To further regulate this representation, we jointly learn both the reconstruction network and the reprojection network during training. A set of light transport modulation blocks is used to modulate the two jointly trained networks in a multi-scale way. Extensive experiments on a large-scale passive NLOS dataset demonstrate the superiority of the proposed method. The code is available at <uri>https://github.com/JerryOctopus/NLOS-LTM</uri>.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"34 ","pages":"410-424"},"PeriodicalIF":0.0,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142879933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-20DOI: 10.1109/TIP.2024.3518100
Zhao Wang;Bolin Chen;Shurun Wang;Shiqi Wang;Yan Ye;Siwei Ma
How to compress face video is a crucial problem for a series of online applications, such as video chat/conference, live broadcasting and remote education. Compared to other natural videos, these face-centric videos owning abundant structural information can be compactly represented and high-quality reconstructed via deep generative models, such that the promising compression performance can be achieved. However, the existing generative face video compression schemes are faced with the inconsistency between the 3D facial motion in the physical world and the face content evolution in the 2D view. To solve this drawback, we propose a 3D-Keypoint-and-2D-Motion based generative method for Face Video Compression, namely FVC-3K2M, which can well ensure perceptual compensation and visual consistency between motion description and face reconstruction. In particular, the temporal evolution of face video can be characterized into separate 3D keypoints from the global and local perspectives, entailing great coding flexibility and accurate motion representation. Moreover, a cascade motion conversion mechanism is further proposed to internally convert 3D keypoints to 2D dense motion, enforcing the face video reconstruction to be perceptually realistic. Finally, an adaptive reference frame selection scheme is developed to enhance the adaptation of various temporal movements. Experimental results show that the proposed scheme can realize reliable video communication in the extremely limited bandwidth, e.g., 2 kbps. Compared to the state-of-the-art video coding standards and the latest face video compression methods, extensive comparisons demonstrate that our proposed scheme achieves superior compression performance in terms of multiple quality evaluations.
{"title":"Ultra-Low Bitrate Face Video Compression Based on Conversions From 3D Keypoints to 2D Motion Map","authors":"Zhao Wang;Bolin Chen;Shurun Wang;Shiqi Wang;Yan Ye;Siwei Ma","doi":"10.1109/TIP.2024.3518100","DOIUrl":"10.1109/TIP.2024.3518100","url":null,"abstract":"How to compress face video is a crucial problem for a series of online applications, such as video chat/conference, live broadcasting and remote education. Compared to other natural videos, these face-centric videos owning abundant structural information can be compactly represented and high-quality reconstructed via deep generative models, such that the promising compression performance can be achieved. However, the existing generative face video compression schemes are faced with the inconsistency between the 3D facial motion in the physical world and the face content evolution in the 2D view. To solve this drawback, we propose a 3D-Keypoint-and-2D-Motion based generative method for Face Video Compression, namely FVC-3K2M, which can well ensure perceptual compensation and visual consistency between motion description and face reconstruction. In particular, the temporal evolution of face video can be characterized into separate 3D keypoints from the global and local perspectives, entailing great coding flexibility and accurate motion representation. Moreover, a cascade motion conversion mechanism is further proposed to internally convert 3D keypoints to 2D dense motion, enforcing the face video reconstruction to be perceptually realistic. Finally, an adaptive reference frame selection scheme is developed to enhance the adaptation of various temporal movements. Experimental results show that the proposed scheme can realize reliable video communication in the extremely limited bandwidth, e.g., 2 kbps. Compared to the state-of-the-art video coding standards and the latest face video compression methods, extensive comparisons demonstrate that our proposed scheme achieves superior compression performance in terms of multiple quality evaluations.","PeriodicalId":94032,"journal":{"name":"IEEE transactions on image processing : a publication of the IEEE Signal Processing Society","volume":"33 ","pages":"6850-6864"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-20DOI: 10.1109/TIP.2024.3515873
Yuzhi Zhao;Lai-Man Po;Xin Ye;Yongzhe Xu;Qiong Yan
Image degradation caused by noise and blur remains a persistent challenge in imaging systems, stemming from limitations in both hardware and methodology. Single-image solutions face an inherent tradeoff between noise reduction and motion blur. While short exposures can capture clear motion, they suffer from noise amplification. Long exposures reduce noise but introduce blur. Learning-based single-image enhancers tend to be over-smooth due to the limited information. Multi-image solutions using burst mode avoid this tradeoff by capturing more spatial-temporal information but often struggle with misalignment from camera/scene motion. To address these limitations, we propose a physical-model-based image restoration approach leveraging a novel dual-exposure Quad-Bayer pattern sensor. By capturing pairs of short and long exposures at the same starting point but with varying durations, this method integrates complementary noise-blur information within a single image. We further introduce a Quad-Bayer synthesis method (B2QB) to simulate sensor data from Bayer patterns to facilitate training. Based on this dual-exposure sensor model, we design a hierarchical convolutional neural network called QRNet to recover high-quality RGB images. The network incorporates input enhancement blocks and multi-level feature extraction to improve restoration quality. Experiments demonstrate superior performance over state-of-the-art deblurring and denoising methods on both synthetic and real-world datasets. The code, model, and datasets are publicly available at https://github.com/zhaoyuzhi/QRNet.
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