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Learned image Compression with Enhanced Dynamic Spatial Aggregation and Asymmetric Entropy model

Learned Image Compression with Enhanced Dynamic Spatial Aggr...

引用

IEEE visual communications and image processing (VCIP)

作者： Yuanton Zhang Nianxiang Fu Guo Yu Xiangdong Lv Huairui Wang Zhenzhong Chen School of Remote Sensing and Information Engineering Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan University Wuhan China School of Computer Science Wuhan University Wuhan China

Learned image compression (LIC) has shown significant potential and better rate-distortion performance than traditional techniques. However, existing CNN-based approaches or window-based self-attention methods can only capture spatial information within fixed ranges. To tackle this limitation, we propose a novel method with dynamic spatial aggregation for transform coding. Our approach introduces enhanced adaptive aggregation, which generates kernel offsets to capture relevant information within content-dependent ranges, improving the transform process. Furthermore, we define a generalized coarse-to-fine entropy model that considers global context, channel-wise information, and spatial context in a coarse-to-fine manner. Additionally, our method takes into full consideration the model’s efficiency and complexity. By introducing the asymmetric entropy model structure and efficient heterogeneous convolution, our approach maintains lower coding complexity and higher decoding speed while ensuring performance. Experimental results demonstrate a substantial improvement in rate-distortion performance achieved by our method when compared to traditional compression methods like VTM and BPG, as well as some LIC methods.

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TSARN: A Joint Temporal-Spatial-Angular Reconstruction Network for Light Field Lenslet Video Compression

TSARN: A Joint Temporal-Spatial-Angular Reconstruction Netwo...

引用

IEEE visual communications and image processing (VCIP)

作者： Huan Li Xinpeng Huang Yongjie Lu Ping An School of Communication and Information Engineering Shanghai University Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Light Field (LF) lenslet videos are capable of capturing the aggregation of light rays in dynamic scenes, providing a more immersive experience. However, the immense data volume of LF lenslet videos poses significant challenges for storage and transmission. Existing methods that utilize traditional video codecs to encode all views fail to meet the demands for efficient compression. This paper proposes a compression framework based on down-sampling, which focuses on designing an efficient reconstruction method at the decoder side. In LF lenslet videos, pixels that are invisible in the current view may be visible in adjacent views angularly and/or in adjacent frames temporally. Therefore, we propose a joint temporal-spatial-angular reconstruction network. In this network, Spatial-Angular Convolutional Module utilizes different forms of convolutions to fully extract spatial-angular features for synthesizing LF views initially; Deformable Convolutional Temporal Fusion Module employs deformable convolutions to align information from different frames and aggregate temporal features; View Refinement Module is finally adopted to further refine the features and reconstruct high-quality LF lenslet videos. Experimental results demonstrate that our proposed LF lenslet video compression framework achieves superior performance, significantly reducing the bitstream.

关键词： Convolutional codes image coding visual communication Video compression Feature extraction Light fields Decoding Video codecs image reconstruction Videos

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ViSeNet: A visual-Semantic Fusion Network for Enhancing Few-Shot Classification

ViSeNet: A Visual-Semantic Fusion Network for Enhancing Few-...

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2025 IEEE International conference on Consumer Electronics, ICCE 2025

作者： Lee, Dongheon Yun, Sangwoo J., Paik Image Processing and Intelligent & System Laboratory Graduate School of Artificial Intelligence Chung-Ang University Seoul Korea Republic of Image Processing and Intelligent & System Laboratory Graduate School of Advanced & Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9798331521165

Few-shot learning addresses the challenge of recognizing novel classes with only a few labeled examples, a critical issue in limited data scenarios. This paper proposes a multi-modal visual and Semantic Network (ViSeNet), which enhances few-shot classification by mining linguistic features and fusing weighted vectors. ViSeNet generates enriched class prototypes by leveraging both visual and semantic information. The linguistic feature mining process utilizes image-to-text models to produce class-specific textual descriptions, which are then transformed into representative semantic embeddings. For visual feature extraction, we employ a pretrained Swin Transformer, while text embeddings are integrated for each class using weighted factors. By combining these modalities, ViSeNet constructs class prototypes that capture both visual and semantic characteristics. Experimental results on benchmark datasets, particularly CIFAR-FS, demonstrate that ViSeNet achieves performance comparable to state-of-the-art approaches. © 2025 IEEE.

关键词： Labeled data

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Lightweight Graph Convolutional Network Based on Multi-Head Residual Attention for Hand Point Classification

Lightweight Graph Convolutional Network Based on Multi-Head ...

引用

IEEE visual communications and image processing (VCIP)

作者： Duc-Chinh Nguyen Manh-Hung Ha Manh-Tuan Do Oscal Tzyh-Chiang Chen Faculty of Applied Sciences International School Vietnam National University Hanoi Vietnam Department of Electrical Engineering National ChungCheng University

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

In this work, we propose a lightweight Graph Convolution Network (GCN) based on residual attention model successfully applied to the classification task of hand point landmarks. The main highlights of our study involve redefining the standard reference point from the initial outer-coordinate origin to the central position within the palm and improving the node features within the graph. Additionally, we construct a graph network capable of classifying points on the hand. This is achieved through a 2-layer structure, which incorporates attention modules to optimize graph information processing, along with a skip connection to prevent feature uniformity within nodes after numerous hidden layers. The experimental results on the ALS dataset indicate that the proposed model yields remarkably high accuracy rates on both the training and test sets, reaching 97.75% and 97.41%, respectively, while keeping the computational complexity (parameters are only 25,874) at an extremely low level. Moreover, the proposed model exhibited high accuracy rates on the Sign Language MNIST dataset, achieving 99.41% on both accuracy and F1-score. Thus, it has great potential to be deployed on portable devices.

关键词： Hands Training Sign language Accuracy Convolution visual communication Computational modeling Information processing Standards Testing

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Performance Evaluation of Speech Enhancement of Single Acoustic Signal Referring to image Information 5

Performance Evaluation of Speech Enhancement of Single Acous...

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5th International conference on Imaging, Signal processing and communications (ICISPC)

作者： Matsumoto, Mitsuharu Univ Electrocommun Dept Informat 1-5-1 Chofugaoka Chofu Tokyo 1828585 Japan

ISBN: (纸本)9781665424257

This paper presents performance evaluation of speech enhancement of an acoustic signal referring to image information. In recent years, we often see smart phones and tablets in everyday life. Smart phones and tablets have a camera and a microphone. Although many speech enhancement techniques use only the audio information from microphones, it is considered that visual information from a camera is also helpful to enhance the speech. Although the frame rate of the normal movie is very low compared to the sampling frequency of the audio signal, it is useful if it is adequately used. We investigated the performance depending on the parameters and confirmed its effectiveness through the experiments.

关键词： Vision-referential speech enhancement Mask information Smart phone Binary mask

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image processing using Cloud for Surveillance 23

Image Processing using Cloud for Surveillance

引用

Proceedings of the 5th International conference on Information Management & Machine Intelligence

作者： Shivam Tiwari Aarhee Phukan T R Vedhavathy Department of Networking and Communications SRM Institute of Science and Technology India Department of Networking and Communication SRM Institute of Science and Technology India

ISBN: (纸本)9798400709418

In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources, there is an increasing need for advanced image processing techniques that can automate the analysis and extraction of valuable information. Amazon Web Services (AWS) Rekognition emerges as a powerful solution in this landscape, offering a comprehensive system for image and video analysis through the lens of machine learning and computer vision. This paper delves into the realm of image processing using AWS Rekognition, unveiling the transformative capabilities of this cloud-based service and its applications in various domains. As we embark on this journey, we will explore the principles, methodologies, and real-world implications of leveraging AWS Rekognition for image analysis.

关键词： AWS Rekognition

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Stochastic Recursive Gradient Descent Optimization-based on Foreground Features of Fisher Vector 14

Stochastic Recursive Gradient Descent Optimization-based on ...

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14th International conference on Digital image processing, ICDIP 2022

作者： Kamaleldin, Mohamed Gamal M. Abu-Bakar, Syed A.R. Sheikh, Usman Ullah Electronics and Communications Engineering Department Arab Academy for Science and Technology Cairo11736 Egypt Computer Vision Video and Image Processing Research Lab School of Electrical Engineering Universiti Teknologi Malaysia Johor Skudai81300 Malaysia

ISBN: (纸本)9781510657564

Human action recognition has been one of the hot topics in computer vision both from the handcrafted and deep learning approaches. In the handcrafted approach, the extracted features are encoded for reducing the size of these features. Amonsgt the state-of-the-art approaches is to encode these visual features using the Gaussian mixture model. However, the size of the codebook is an issue in terms of the computation complexity, especially for large-scale data as it requires encoding using a large codebook. In this paper, we introduced the use of different optimizers to reduce the codebook size while boosting its accuracy. To illustrate the performance, first we use the improved dense trajectories (IDT) to extract the handcrafted features. This is followed with encoding the descriptor using Fisher kernel-based codebook using the Gaussian mixture model. Next, the support vector machine is used to classify the categories. We then use and compare five different Stochastic gradient descent optimization techniques to modify the number of Gaussian components. In this manner we are able to select the discriminative foreground features (as represented by the final number of Gaussian components), and omit the background features. Finally, to show the performance improvement of the proposed method, we implement this technique to two datasets UCF101 and HMDB51. © 2022 SPIE.

关键词： Support vector machines

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What Pushes Self-supervised image Representations Away? 28th

What Pushes Self-supervised Image Representations Away?

引用

28th International conference on Neural Information processing

作者： Zielinski, Bartosz Gorszczak, Michal Jagiellonian Univ Fac Math & Comp Sci Lojasiewicza 6 PL-30428 Krakow Poland

ISBN: (纸本)9783030923068;9783030923075

Self-supervised models provide on par or superior results to their fully supervised competitors, yet it is unclear what information about images they contain. As a result, a visual probing framework was recently introduced to probe image representations for interesting visual features. While visual probing provides information about semantic knowledge, complexity, and consistency, it does not directly and exhaustively explain which visual features push self-supervised image representations away and which are neutral. In this paper, we fill this gap by proving a method that removes particular visual features from the image and analyzes how such a distortion influences the representation. Our key findings emphasize that discrepancies in features like lines and forms push self-supervised representations away more than brightness, color, shape, and especially texture changes. Our work is complementary to visual probing and provides more direct explanations of the mechanisms behind the contrastive loss.

关键词： Explainability Self-supervised representation Computer vision Deep learning

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DL-Assisted ROP Screening Technique 14th

DL-Assisted ROP Screening Technique

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14th International Joint conference on Biomedical Engineering Systems and Technologies

作者： Kumar, Vijay Patel, Het Azad, Shorya Paul, Kolin Surve, Abhidnya Chawla, Rohan Indian Inst Technol Delhi Khosla Sch Informat Technol New Delhi India Indian Inst Technol Delhi Dept Comp Sci & Engn New Delhi India All India Inst Med Sci Delhi Dr Rajendra Prasad Ctr Ophthalm Sci New Delhi India

ISBN: (纸本)9783031206634;9783031206641

Retinopathy of Prematurity (ROP) is the most common cause of visual impairment among premature babies throughout the world. The consequence of ROP impairment can be minimized by performing suitable screening and treatment. However, due to deficiency of health care resources, many of these premature infants remain unidentified after birth. As a result, ROP-induced visual impairment is much more prevalent in these babies. We propose a robust and intelligent approach based on deep artificial intelligence and computer vision to automatically recognise the optical disk (OD) and retinal blood vessels and categorise the severe severity (Zone-1) of ROP patients in this study. We report empirical evidence using premature infant retina images from a nearby hospital to evaluate and validate the proposed approach. The YOLO-V5 prediction model identifies the OD from premature infants retina images, according to our results. Furthermore, the preterm infants' fundus images were perfectly segmented by the computer vision-based system, which effectively separated the retinal vessels. Our system is able to obtain an accuracy of 82.5% in the Zone-1 occurrence of ROP.

关键词： Fundus image Retinopathy of Prematurity (ROP) Computer Aided Diagnosis (CAD) image processing Deep Learning (DL) YOLO-v5

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Energy-Efficient Video Streaming: A Study on Bit Depth and Color Subsampling

Energy-Efficient Video Streaming: A Study on Bit Depth and C...

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IEEE visual communications and image processing (VCIP)

作者： Hadi Amirpour Lingfeng Qu Jong Hwan Ko Cosmin Stejerean Christian Timmerer Christian Doppler Laboratory ATHENA Alpen-Adria-Universitat Klagenfurt Austria Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Department of Electrical and Computer Engineering Sungkyunkwan University South Korea Meta Platforms Inc. Menlo Park USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

As video dimensions – including resolution, frame rate, and bit depth – increase, a larger bitrate is required to maintain a higher Quality of Experience (QoE). While videos are often optimized for resolution and frame rate to improve compression and energy efficiency, the impact of color space is often overlooked. Larger color spaces are essential for avoiding color banding and delivering High Dynamic Range (HDR) content with richer, more accurate colors, although this comes at the cost of higher processing energy. This paper investigates the effects of bit depth and color subsampling on video compression efficiency and energy consumption. By analyzing different bit depths and subsampling schemes, we aim to determine optimized settings that balance compression efficiency with energy consumption, ultimately contributing to more sustainable and high-quality video delivery. We evaluate both encoding and decoding energy consumption and assess the quality of videos using various metrics including PSNR, VMAF, ColorVideoVDP, and CAMBI. Our findings offer valuable insights for video codec developers and content providers aiming to improve the performance and environmental footprint of their video streaming services.

关键词： Energy consumption image coding image color analysis Energy resolution Bit rate Color Streaming media Energy efficiency Decoding Quality of experience

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