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Multi-QP Rate Distortion Optimized Quantization Using Deep Learning

Multi-QP Rate Distortion Optimized Quantization Using Deep L...

引用

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

作者： Afro, Pierre-Alain Strus, Loic Bonnaud, Laurent Caplier, Alice Robin, Frederic Allegro DVT France Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab France

ISBN: (纸本)9798350359855

RDOQ (Rate Distortion Optimized Quantization) is an efficient encoding tool that can be used with several codecs such as H.264/AVC, H.265/HEVC or AV1. Although this algorithm can significantly reduce the bit rate, its complexity is a limitation for video coding hardware solutions. Studies have succeeded in simplifying RDOQ allowing its integration into HM, the reference software implementation of H.265/HEVC. However, its iterative and sequential behavior does not allow an efficient hardware implementation. With the advent of machine learning, neural networks have been introduced to mimic the RDOQ algorithm in a parallel way. Previous proposed Deep-Learning based RDOQ frameworks still need to be trained for each Quantization Parameter (QP) which is a huge limitation for hardware implementation. To address this issue, we propose a Multi-QP Deep Learning based RDOQ, trained with data extracted from 4 QPs and achieving targeted performances with QP ranging from 22 to 37. Our multi-QP model almost reaches HM-RDOQ performance in terms of BD-Rate savings. © 2023 IEEE.

关键词： image coding

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Towards Deep Reference Frame in Versatile Video Coding NNVC

Towards Deep Reference Frame in Versatile Video Coding NNVC

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

作者： Bao, Weijie Jia, Jianghao Meng, Wenhui Liu, Zizheng Xu, Xiaozhong Liu, Shan Chen, Zhenzhong Wuhan University School of Remote Sensing and Information Engineering Wuhan China Tencent Media Lab Shenzhen China Tencent Media Lab Palo AltoCA United States

ISBN: (纸本)9798350359855

In this paper, we propose a deep reference frame generation method that aims to enhance bi-direction inter prediction under random access configuration in the latest video coding standard, Versatile Video Coding. Specifically, a pair of neighboring reconstructed frames are selected from decoded picture buffer and put into an optical-flow-based interpolation network to synthesize a new frame, similar to the current to-be-coded frame. Subsequently, this synthesized frame is incorporated into two-sided picture reference lists as additional reference frames. The proposed method is employed in both the encoding and decoding processes to eliminate bitstream signaling for supplementary information. The Small Ad-hoc Deep-Learning Library is utilized for implementing the proposed method. Experimental results demonstrate 3.67%/7.34%/6.51% coding efficiency improvements for Y/U/V components under the random access configuration when compared to the Versatile Video Coding NNVC reference software VTM-11_NNVC-5.0. © 2023 IEEE.

关键词： Video signal processing

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Cheque Information processing and Validation using Deep Learning 2

Cheque Information Processing and Validation using Deep Lear...

引用

2nd IEEE Global conference on communications and Information Technologies, GCCIT 2024

作者： Suganthi, K. Shenoy, Darshan N. Vellore Institute of Technology School of Electronics Engineering Chennai600127 India

ISBN: (纸本)9798350388916

Understanding document images, such as invoices, is a difficult task. It necessitates reading the text as well as comprehending the document's general structure. Conventional techniques first extract text using Optical Character Recognition (OCR), yet this method has drawbacks. In addition to being computationally costly, OCR can have trouble converting between various languages and document formats and introduce errors that can compound throughout the analysis *** is an OCR-free VDU (visual Document Understanding) model that challenges this paradigm. It does not even bother with the text extraction stage;it analyzes the document image straight. This creative strategy produces notable advancements. Donut performs at the cutting edge, which means it is quick and precise. This is a significant development in the realm of visual document understanding since it enables Donut to handle a greater variety of languages and document types. The model has been tested using BLEU scores and ROUGE scores over 3 parameters namely - payee name, amount in words and amount in figure. The model has also been tested over variety of sample sizes ranging from 50 to 1000. © 2024 IEEE.

关键词： Optical character recognition

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Effectively Filtering images for Better Multi-modal Knowledge Graph 6th

Effectively Filtering Images for Better Multi-modal Knowledg...

引用

6th Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International conference on Web and Big Data (APWeb-WAIM)

作者： Peng, Huang Xu, Hao Tang, Jiuyang Wu, Jibing Huang, Hongbin Natl Univ Def Technol Lab Big Data & Decis Changsha 410000 Peoples R China

ISBN: (纸本)9789819913534;9789819913541

The existing multi-modal knowledge graph construction techniques have become mature for processing text modal data, but lack effective processing methods for other modal data such as visual modal data. Therefore, the focus of multi-modal knowledge graph construction lies in image and image and text fusion processing. At present, the construction of multi-modal knowledge graph often does not filter the image quality, and there are noises and similar repetitive images in the image set. To solve this problem, this paper studies the quality control and screening of images in the construction process of multi-modal knowledge graph, and proposes an image refining framework of multi-modal knowledge graph, which is divided into three modules. The final experiment proves that this framework can provide higher quality images for multi-modal knowledge graphs, and in the benchmark task of multi-modal entity alignment, the effect of entity alignment based on the multi-modal knowledge graphs constructed in this paper has been improved compared with previous models.

关键词： Multi-modal knowledge graph Clustering algorithm image-text matching Entity alignment

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Semi-supervised Multi-domain Learning for Medical image Classification 5th

Semi-supervised Multi-domain Learning for Medical Image Clas...

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5th International conference on Recent Trends in image processing and Pattern Recognition (RTIP2R)

作者： Chavhan, Ruchika Banerjee, Biplab Das, Nibaran Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland Indian Inst Technol Ctr Machine Intelligence & Data Sci Mumbai Maharashtra India Jadavpur Univ Dept CSE Kolkata India

ISBN: (纸本)9783031235986;9783031235993

The limitations of domain dependence in neural networks and data scarcity are addressed in this paper by analyzing the problem of semi-supervised medical image classification across multiple visual domains using a single integrated framework. Under this premise, we learn a universal parametric family of neural networks, which share a majority of their weights across domains by learning a few adaptive domain-specific parameters. We train these universal networks on a suitable pretext task that captures a meaningful representation for image classification and further fine-tune the networks using a small fraction of training data. We perform our experiments on five medical datasets spanning breast, cervical, and colorectal cancer. Extensive experiments on architectures of domain-adaptive parameters demonstrate that our data-deficient universal model performs equivalently to a fully supervised setup, rendering a semi-supervised multi-domain setting with lower numbers of training samples for medical data extremely feasible in the real world.

关键词： Multi-domain learning Semi-supervised learning Medical image classification

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8th International conference on Computer Vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th International conference on Computer Vision and image processing, CVIP 2023

ISBN: (纸本)9783031581731

The proceedings contain 140 papers. The special focus in this conference is on Computer Vision and image processing. The topics include: A Novel Framework for Cognitive Load Estimation from Electroencephalogram Signals Utilizing Sparse Representation of Brain Connectivity;MCCNet: A Multi-scale Cross Connection Network for Low-Light image Enhancement;feature Fusion and Multi-head Attention Based Hindi Captioner;Payload Length and Location Identification Using a Novel CNN and Re-embedding Strategy in Stego images Created by Content Adaptive Steganographic Algorithms;ConvMTL: Multi-task Learning via Self-supervised Learning for Simultaneous Dense Predictions;driver Drowsiness Detection Using Vision Transformer;automated Cricket Commentary Generation for Videos;image Captioning with visual Positional Embedding and Bi-linear Pooling;Cartilage Segmentation from MRI images Towards Prediction of Osteoarthritis;Reinforcement Algorithm-Guided ROI Extraction of Fingerprint Biometric Data;improved Metric Space for Shape Correspondence;an Integrated Approach: Combining GrabCut and Contour-Matching for Hand Gesture Segmentation in Indian Sign Language;identity Preserved Expressive Talking Faces with Synchrony;isolated Sign Language Recognition Using Deep Learning;a Gradient-Based Approach to Interpreting visual Encoding Models;adjust Your Focus: Defocus Deblurring from Dual-Pixel images Using Explicit Multi-Scale Cross-Correlation;Efficient Seizure Prediction from images of EEG Signals Using Convolutional Neural Network;coping with Increased Levels of Label Noise in Facial Expression Recognition;experimental Evaluation of Needle Tip Prediction Using Kalman Filtering Approach;a Deep Face Antispoofing System with Hardware Implementation for Real-Time Applications;Bit Plane Segmentation and LBP-Based Coverless Video Steganography for Secure Data Transmission;a Deep Learning Approach to Enhance Semantic Segmentation of Bacteria and Pus Cells from Microscopic Urine Smear images Using

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8th International conference on Computer Vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th International conference on Computer Vision and image processing, CVIP 2023

ISBN: (纸本)9783031585340

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Design of UAV Indoor Vision and UWB Integrated Inspection Algorithm 23

Design of UAV Indoor Vision and UWB Integrated Inspection Al...

引用

7th International conference on High Performance Compilation, Computing and communications, HP3C 2023

作者： Yang, Yejin Huang, Yuejin Ye, Miao Cheng, Jin Sun, Shiquan School of Information and Communications Guilin University of Electronic Technology China

ISBN: (纸本)9781450399883

This paper proposes a visual and ultra-wideband (UWB) fusion-based inspection algorithm for addressing problems of lighting, shadows, and visual blur that may occur during indoor Unmanned Aerial Vehicle (UAV) inspections. The method includes two stages: line tracking and positioning. At the line tracking stage, a downward-facing camera is used as the visual sensor for image segmentation. When an anomaly occurs in the visual sensor, it automatically switches to UWB localization and navigation mode while continuously reading the feedback from the visual sensor until it returns to normal. At the positioning stage, AprilTag and UWB are merged for location, enabling more precise UAV positioning by fusing the input results of AprilTag and UWB. Experimental results show that the proposed UAV inspection algorithm can effectively solve the problems of lighting, shadows, and visual blur that may occur during visual sensor inspection. The algorithm is based on the fusion of visual and UWB sensors, which improves system reliability and stability. © 2023 ACM.

关键词： image segmentation

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8th International conference on Computer Vision and image processing, CVIP 2023

8th International Conference on Computer Vision and Image Pr...

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8th International conference on Computer Vision and image processing, CVIP 2023

ISBN: (纸本)9783031581809

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PTS-LIC: Pruning Threshold Searching for Lightweight Learned image Compression

PTS-LIC: Pruning Threshold Searching for Lightweight Learned...

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

作者： Luo, Ao Sun, Heming Liu, Jinming Lin, Fangzheng Katto, Jiro Yokohama National University Faculty of Engineering Kanagawa Japan Shanghai Jiao Tong University China Waseda University Department of Computer Science and Communication Engineering Tokyo Japan Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (纸本)9798350359855

Learned image Compression (LIC), which uses neural networks to compress images, has experienced significant growth in recent years. The hyperprior-module-based LIC model has achieved higher performance than classical codecs. However, the LIC models are too heavy (in calculation and parameter amounts) to apply to edge devices. To solve this problem, some former papers focus on structural pruning for LIC models. However, they either cause noticeable performance decrement or neglect the appropriate pruning threshold for each LIC model. These problems keep their pruning results sub-optimal. This paper proposes a Pruning Threshold Searching on the hyperprior module for different-quality LIC models. Our method removes most parameters and calculations while keeping the performance the same as the models before pruning. We removed at least 49.8% of parameters and 28.5% of calculations for the Channel-Wise-Context-Model-based models and 29.1% of parameters for the Cheng-2020 models. © 2023 IEEE.

关键词： image compression

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