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NeRV++: An Enhanced Implicit Neural Video Representation

NeRV++: An Enhanced Implicit Neural Video Representation

引用

IEEE visual communications and image processing (VCIP)

作者： Ahmed Ghorbel Wassim Hamidouche Luce Morin Univ Rennes INSA Rennes CNRS IETR Rennes France

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Neural fields, also known as implicit neural representations (INRs), have shown a remarkable capability of representing, generating, and manipulating various data types, allowing for continuous data reconstruction at a low memory footprint. Though promising, INRs applied to video compression still need to improve their rate-distortion performance by a large margin, and require a huge number of parameters and long training iterations to capture high-frequency details, limiting their wider applicability. Resolving this problem remains a quite challenging task, which would make INRs more accessible in compression tasks. We take a step towards resolving these shortcomings by introducing neural representations for videos (NeRV)++, an enhanced implicit neural video representation (INVR), as more straightforward yet effective enhancement over the original NeRV decoder architecture, featuring separable conv2d residual blocks (SCRBs) that sandwiches the upsampling block (UB), and a bilinear interpolation skip layer for improved feature representation. NeRV++ allows videos to be directly represented as a function approximated by a neural network, and significantly enhance the representation capacity beyond current INR-based video codecs. We evaluate our method on UVG, MCL JVC, and Bunny datasets, achieving competitive results for video compression with INRs. This achievement narrows the gap to autoencoder-based video coding, marking a significant stride in INR-based video compression research. The code of NeRV++ is available on GitHub.

关键词： Video coding Training image coding Limiting visual communication Neural networks Rate-distortion Video compression Video codecs Software development management

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Dynamic Volumetric Video Coding with Tensor Decomposition

Dynamic Volumetric Video Coding with Tensor Decomposition

引用

IEEE visual communications and image processing (VCIP)

作者： Ju-Yeon Shin Yeoneui Kim Je-Won Kang Gun Bang Dept. of Electronic and Electrical Engineering and Graduate Program in Smart Factory Ewha Womans University Seoul South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recently, volumetric video coding based on neural radiance fields has gained significant attention for storing and transmitting three-dimensional (3D) scenes captured from multi-view video. Because the neural networks are trained to produce novel view synthesis of surrounding 3D scenes, compressing the model and then rendering the colors and geometry through the decompressed model can be utilized as a 3D video coding system. However, although this approach provides superior performance compared to conventional 3D video coding standards using depth video, challenges remain in reducing overall model sizes to improve coding efficiency. In this paper, we propose a novel dynamic volumetric video coding technique that employs a Group of Volume (GoV) to divide multi-view video sequences into smaller chunks, addressing complex temporal dynamics. Our method uses volumetric video features represented with 3D spatial and temporal tensor matrices and vectors and encodes them with the GoVs. The tensors are compressed by existing 2D video codec, allowing for fast rendering and easing deployment. Experimental results validate that our method not only reduces memory footprint but also maintains high-quality rendering as compared to state-of-the-art studies.

关键词： Video coding Solid modeling Three-dimensional displays image coding Tensors visual communication Video sequences Rendering (computer graphics) Video codecs Standards

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VoiceCap: Automatic image Captioning and Voice Generation

VoiceCap: Automatic Image Captioning and Voice Generation

引用

International conference on Computer and communications Technologies (ICCCT)

作者： Sankari Subbiah Sudha G Saranya S Bharathan S Department of Information Technology Sri Sai Ram Engineering College Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Engineering College Chennai India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

One of the most in-demand resources in the contemporary period is picture captioning. In addition, there are built-in apps that use models from deep neural networks to create and provide a caption for a specific picture. picture captioning refers to the process of creating a description for a picture. It entails identifying key elements in a picture, as well as those items properties and the connections between them. Syntactically and semantically sound sentences are produced by it. VoiceCap is an innovative software for smartphones that uses sophisticated deep learning algorithms to automatically provide audio descriptions for photos and videos. Making sure people with all kinds of visual impairments can enj oy visual material to the fullest is its primary goal in advocating accessibility and inclusion. The VoiceCap cross-platform solution uses a combination of computer vision and natural language processing algorithms to generate spoken and text-based captions for user-uploaded photographs and videos. The captions' correctness and relevance to context are guaranteed by a state-of-the-art Vision-Encoder-Decoder Model.

关键词： Deep learning visualization Computational modeling visual impairment Software algorithms Natural language processing Software Smart phones Videos Context modeling

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Compressing 3D Gaussian Splatting via a Generalizable Neural Coder

Compressing 3D Gaussian Splatting via a Generalizable Neural...

引用

IEEE visual communications and image processing (VCIP)

作者： Junteng Zhang Tong Chen Hao Zhu Dong Wang Dandan Ding Zhan Ma Nanjing University Nanjing China Interdisciplinary Research Center for Future Intelligent Chips (Chip-X) Nanjing University Suzhou China OPPO Inc. Nanjing China Hangzhou Normal University Hangzhou China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

As a promising technique for 3D representation, 3D Gaussian Splatting (3DGS) offers fast rendering speed and high fidelity while generating large data volumes. This challenges storage and transmission, so an efficient compression solution is required. Existing implicit methods require pre-scene optimization (online), leading to a long optimization time. By contrast, this paper regards the 3DGS as a point cloud and pre-trains a generalizable (offline) neural coder for compression. After obtaining the 3DGS representation, we focus on the data compression process, which is friendly to applications already equipped with a PCC codec. The neural coder employed is extended from a typical AIbased point cloud compression method, which uses a multiscale and multistage framework to exploit spatial correlations across scales and stages for conditional coding. Experimental results show that our method significantly outperforms existing 3DGS representations without compromising fidelity, achieving more than 39× and 6.8× compression ratio compared to the original 3DGS and SOTA Scaffold-GS, respectively. More importantly, our approach does not require additional time to optimize the compression model.

关键词： Point cloud compression image coding Three-dimensional displays Correlation Codecs visual communication Rendering (computer graphics) Optimization

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Enhancing image Denoising Performance through a Family of Algorithms

Enhancing Image Denoising Performance through a Family of Al...

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International conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal processing (PCEMS)

作者： Omkar Gupta Irika Nikhita Kalapala Kishor M. Bhurchandi Department of Electronics and Communication Engineering Visvesvaraya National Institute of Technology Nagpur India

images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image *** efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising.

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Remote Temperature Monitoring for Infant Incubator using Thermal Camera 14

Remote Temperature Monitoring for Infant Incubator using The...

引用

14th Biomedical Engineering International conference (BMEiCON)

作者： Chuenpirom, Thanaporn Srirussamee, Kasama Pintavirooj, Chuchart Kings Mongkut Inst Technol Ladkrabang KMITL Sch Engn Bangkok Thailand

ISBN: (纸本)9781665489034

Health concerns over the premature babies are their immature organs and low birth weight. As a result, these babies may have difficulty in breathing, maintaining their healthy body temperature or being light sensitive. Infant incubator has been designed to overcome these issues. It is a medical device that could provide healthy environment, such as light, temperature and humidity. Moreover, it is also able to control and detect any changes which affect the baby's health. Health monitoring systems are critical in modern incubators, which could involve a set of sensors to read and send physiological information, such as temperature. It has also been proposed that establishing remote monitoring system via global system for mobile communications (GSM) would be beneficial for the incubators as it could send an alarm to the doctors and parents in the event that unhealthy vital parameters have been detected. Although a variety of vital parameters are included in the monitoring features of some modern incubators, the capability to detect body temperature of the babies are limited. Hence, this project proposes the development of an effective low-cost smart infant incubator that will benefit the body temperature monitoring of newborn babies using remote IoT-based monitoring system. The monitoring system is controlled through the use of a Raspberry Pi attached to a visual camera and a thermal IR camera with temperature sensor MLX90640, which was able to measure the body temperature remotely. The software of this smart infant incubator was used to improve monitoring system, which involves image processing techniques. The results of measured ambient temperature were found to be accurate and reliable. The prototypical incubator has been designed and manufactured with affordable cost, and the control system of the incubator was able to work comparably to other incubators with potential capability to measure body temperature of the infant.

关键词： Infant incubator IoT image processing

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Multi-Direction Strategy with Honey Badger Algorithm and Convolutional Neural Network for Industrial Robot visual Servo image processing

Multi-Direction Strategy with Honey Badger Algorithm and Con...

引用

Intelligent Algorithms for Computational Intelligence Systems (IACIS), International conference on

作者： Hao Chen Deppartment of Engineering & Mechanics Jilin Communications Polytechnic Changchun China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The Industrial robot visual servo image processing requires highly autonomous and intelligence robotic manipulators, with goal of performing manipulation tasks independently without human interventions. However, limit efficiency for large scale, sensitive to noise in input data which affect classification accuracy. This paper, proposed Multi-Direction Strategy with Honey Badger Algorithm (MHBA) and Convolutional Neural Network (CNN) for classification is effectively explore the hyper parameter space of CNN technique is help achieve classification accuracy. The MHBA ability to adapt and explore multiple directions in the parameter space makes CNN maximum efficient to variation in input data. The MHBA model is applied to large scale CNN model is efficiently narrowing down the search space and balances exploration and exploitation allowing for search space. The Pre-processing using linear transformation such as translation or rotation help in adjusting the image dimension while maintaining the essential content. The proposed MHBA-CNN technique for industrial robot is achieving better outcomes such as Mean Absolute Error (MAE) of 2.03, Mean Absolute Scaled Error (MASE) of 3.45, R 2 score of 2.45 on raw dataset. The existing technique such as Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) are evaluated of proposed method.

关键词： Adaptation models visualization Recurrent neural networks Accuracy Industrial robots Space exploration Classification algorithms Convolutional neural networks Servomotors Long short term memory

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Turkish image Captioning with Vision Transformer Based Encoders and Text Decoders

Turkish Image Captioning with Vision Transformer Based Encod...

引用

IEEE Signal processing and communications Applications (SIU)

作者： Serdar Yıldız Abbas Memiş Songül Varlı Bilgisayar Mühendisliği Bölümü Yıldız Teknik Üniversitesi İstanbul Türkiye BİLGEM TÜBİTAK Kocaeli Türkiye Bilgisayar Mühendisliği Bölümü İstanbul Üniversitesi İstanbul Türkiye Türkiye Sağlık Enstitüleri Başkanlığı İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

image captioning is defined as the process of describing of images by computer systems automatically. Thus, visual information regarding the content of the images is expressed in textual form. This paper presents a deep learning-based Turkish image captioning study implemented by using vision transformers and text decoders. In the proposed study, images are initially encoded with a vision transformer-based module. Afterwards, the features of the encoded image are normalized by passing them through a feature projection module. In the final stage, image captions are generated via a text decoder block. To test the performance of the Turkish image captioning system presented in this paper, TasvirEt, a benchmark dataset consisting of Turkish image captions, was used. In the tests performed, quite successful results were observed and a BLEU-1 value of 0.3406, a BLEU-2 value of 0.2110, a BLEU-3 value of 0.1253, a BLEU-4 value of 0.0690, a METEOR value of 0.1610, a ROUGE-L value of 0.3145 and a CIDEr value of 0.3879 were measured.

关键词： Computer vision visualization Signal processing Benchmark testing Transformers Decoding Meteors

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Cleaning Landmark and Geolocation Dataset using Deep Learning Methods 29

Cleaning Landmark and Geolocation Dataset using Deep Learnin...

引用

29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Taskin, Berk Karsligil, M. Elif Yildiz Tekn Univ Bilgisayar Muhendisligi Bolumu Akilli Sistemler Lab Istanbul Turkey

ISBN: (纸本)9781665436496

In this paper, a deep learning based approach to dataset cleaning has been explained by utilizing Google Landmark Challenge dataset v2, which is a noisy dataset that consists of images photographed by people. Study also includes improvements on the concept of image classification and recognition for large noisy datasets mainly revolving around the approach to dataset cleaning which are also listed. Results achieved using this approach has been detailed with both quantitative methods like graphs regarding the reduction of classes and number of eliminated noisy images and qualitative methods like visual analysis of said images. In conclusion, it is observed that using confidence score outputs of a deep learning network, it is possible to remove noisy samples from a dataset. This paper also includes the specific threshold values for the achieved results on this dataset using explained model architecture for better reproducibility.

关键词： Dataset cleaning image classification Google Landmark Challenge EfficientNet

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PDCE: Patch-wise Dynamic Curve Estimation for Low-Light image Enhancement

PDCE: Patch-wise Dynamic Curve Estimation for Low-Light Imag...

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

作者： Ruiyuan Chen Zhixin Li Han Zeng Yifan Liu Tao He Tiecheng Song School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light image enhancement (LLIE) can be reformulated as an image-specific curve estimation (CE) problem. Traditional CE-based methods struggle with issues such as uniform processing across different regions, static parameter estimation, and lack of effective global semantic enhancement. To address these limitations, we propose a novel unsupervised learning framework, Patch-wise Dynamic Curve Estimation (PDCE), which dynamically adjusts and optimizes enhancement curves according to local patch brightness and the iteration process. Specifically, we present a Vision-Language Curve Discriminator (VLCD), which dynamically determines the curve type for each patch, avoiding uniformly applying the curve on the whole image. We introduce a Curve Parameter Estimator (CPE), which dynamically updates curve parameters and adjusts enhancement effects based on the output of the previous iteration. Furthermore, we design a visual State Space-based Semantic Enhancement Module (VSEM), which captures global receptive fields and enriches semantic features through the Mamba-based U-Net architecture. Extensive experimental results show the superiority of our PDCE over state-of-the-art methods for LLIE.

关键词： visualization Parameter estimation Semantics Estimation Speech enhancement Signal processing Iterative methods Unsupervised learning image enhancement Optimization

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