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Polynomial image-Based Rendering for non-Lambertian Objects

Polynomial Image-Based Rendering for non-Lambertian Objects

引用

IEEE visual communications and image processing (VCIP)

作者： Sarah Fachada Daniele Bonatto Mehrdad Teratani Gauthier Lafruit Laboratory of Image Synthesis and Analysis Université Libre de Bruxelles Brussels Belgium

ISBN: (纸本)9781728173221

Non-Lambertian objects present an aspect which depends on the viewer's position towards the surrounding scene. Contrary to diffuse objects, their features move non-linearly with the camera, preventing rendering them with existing Depth image-Based Rendering (DIBR) approaches, or to triangulate their surface with Structure-from-Motion (SfM). In this paper, we propose an extension of the DIBR paradigm to describe these non-linearities, by replacing the depth maps by more complete multi-channel “non-Lambertian maps”, without attempting a 3D reconstruction of the scene. We provide a study of the importance of each coefficient of the proposed map, measuring the trade-off between visual quality and data volume to optimally render non-Lambertian objects. We compare our method to other state-of-the-art image-based rendering methods and outperform them with promising subjective and objective results on a challenging dataset.

关键词： Surface reconstruction Three-dimensional displays visual communication Volume measurement image processing Rendering (computer graphics) Cameras

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Application of Computer Digital Simulation Technology in the Research of Martial arts Action Structure image 16

Application of Computer Digital Simulation Technology in the...

引用

16th IEEE International Wireless communications and Mobile Computing conference (IEEE IWCMC)

作者： Wang, Qingbao Baicheng Normal Univ Coll Phys Educ Baicheng 137000 Jilin Peoples R China

ISBN: (纸本)9781728131290

The reference of Martial arts action structure to assist the artificial correction is poor, for this problem, in this paper, a new method of Martial arts action structure analysis and reconstruction based on computer numerical simulation is proposed. Firstly, three-dimensional visual image information acquisition of martial arts is carried out, and adaptive threshold decomposition and wavelet analysis are used to perform noise reduction pretreatment. The original domain feature point library is formed by the original separation of the domain feature points of the martial arts action structure image. Then, the edge contour feature extraction method is used to extract the contour features of the martial arts in the image, input into the expert system of the orthodontics, carry on the visual analysis and correction, and realize the Martial arts action structure analysis and the feature reconstruction identification. The results of computer simulation show that this method can be used to reconstruct the martial arts structure and improve the quantitative analysis ability of martial arts. The signal-to-noise ratio of the output image is high, and the recognition performance of the image processing and motion is better.

关键词： computer numerical simulation technology Martial arts sports training structural characteristics action identification

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History-Based MVP (HMVP) for Gradual Decoding Refresh of VVC

History-Based MVP (HMVP) for Gradual Decoding Refresh of VVC

引用

IEEE visual communications and image processing (VCIP)

作者： Limin Wang Seungwook Hong Krit Panusopone Multimedia Technologies Research Nokia San Diego USA

ISBN: (纸本)9781728173221

Versatile Video Coding (VVC) is a new international video coding standard. One of the functionalities that VVC supports is so called Gradual Decoding Refresh (GDR). GDR is mainly for (ultra) low-delay applications. As the latest video coding standard, VVC employs many new and advanced coding tools. Among them is HMVP (History-based Motion Vector Prediction), which however can cause leaks for GDR applications. This paper analyzes the leak problem associated with HMVP for GDR and proposes suggestions on how to use HMVP for GDR applications.

关键词： Video coding image coding visual communication Decoding Standards

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Cross-Block Difference Guided Fast CU Partition for VVC Intra Coding

Cross-Block Difference Guided Fast CU Partition for VVC Intr...

引用

IEEE visual communications and image processing (VCIP)

作者： Hewei Liu Shuyuan Zhu Ruiqin Xiong Guanghui Liu Bing Zeng University of Electronic Science and Technology of China Chengdu China Peking University Beijing China

ISBN: (纸本)9781728173221

In this paper, we propose a new fast CU partition method for VVC intra coding based on the cross-block difference. This difference is measured by the gradient and the content of sub-blocks obtained from partition and is employed to guide the skipping of unnecessary horizontal and vertical partition modes. With this guidance, a fast determination of block partitions is accordingly achieved. Compared with VVC, our proposed method can save 41.64% (on average) encoding time with only 0.97% (on average) increase of BD-rate.

关键词： image coding visual communication

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SA(2,R) Multi-scale contour registration based on EM Algorithm

SA(2,R) Multi-scale contour registration based on EM Algorit...

引用

IEEE visual communications and image processing (VCIP)

作者： Khaoula Sakrani Sinda Elghoul Sarra Falleh Faouzi Ghorbel CRISTAL Laboratory GRIFT Research Group National School of Computer Science (ENSI) Manouba Tunisia

ISBN: (纸本)9781728173221

Here we propose a novel affine registration method for planar curves. It is based on a pseudo-inverse algorithm applied to the source and target curves in their multi-scale version. The proposed registration system selects the relevant scales in the optimized L 2 distances. The retrieved smoothing parameters are realized with the Gaussian Expectation-Maximization (EM) algorithm. We resolve the global system, formed by equations corresponding to EM selected scales.

关键词： Linear systems Smoothing methods image recognition visual communication Robot sensing systems Complexity theory Remote sensing

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ARITM: A Perceptual Quality Magnification of Noise Suppressed Digital images using Adaptive Recursion and image Transformation Model

ARITM: A Perceptual Quality Magnification of Noise Suppresse...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International conference on

作者： K. Kannan G. Divya L. Chithra V. Aruna V. Mohanavel Department of ECE R.M.K. College of Engineering and Technology Puduvoyal Chennai Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of ECE S.A.Engineering College Chennai Department of ECE Panimalar Engineering College Chennai Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Mechanical Engineering Chandigarh University Mohali Punjab India

In the realm of digital image processing and enhancement, the relentless pursuit of achieving higher perceptual quality in noise-suppressed images has remained a compelling challenge. This abstract presents a novel approach that leverages the synergy between adaptive recursion and a sophisticated image transformation model to magnify the perceptual quality of noise-suppressed digital images. The proliferation of digital imagery in various applications, from medical diagnostics to multimedia content, necessitates the development of advanced techniques to preserve and enhance image fidelity. While noise suppression techniques have made significant strides in reducing unwanted artifacts, they often introduce trade-offs between noise reduction and preservation of image details. Our proposed method begins by employing adaptive recursion, where the image is divided into local regions, each analyzed independently for noise characteristics and visual features. Adaptive recursion ensures that noise reduction is applied selectively, preserving important image details, even in challenging scenarios. The synergy between adaptive recursion and the image transformation model is a key highlight of our approach. By iteratively applying the transformation model within each local region, we adaptively enhance image features that contribute to perceptual quality, such as sharpness, contrast, and color fidelity. This adaptive enhancement process ensures that image details are magnified while suppressing noise artifacts, ultimately resulting in images of superior perceptual quality. Our experimental results, conducted on diverse datasets with various levels of noise, demonstrate significant improvements in perceptual quality compared to state-of-the-art methods. Additionally, we evaluate the computational efficiency of our approach, highlighting its feasibility for real-time and high-throughput image enhancement applications.

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Learned Multi-Field De-Interlacing with Feature Alignment via Deformable Residual Convolution Blocks

Learned Multi-Field De-Interlacing with Feature Alignment vi...

引用

IEEE visual communications and image processing (VCIP)

作者： Ronglei Ji A.Murat Tekalp Koc University Istanbul Turkey

ISBN: (纸本)9781728173221

Deinterlacing continues to be an important problem of interest since many digital TV broadcasts and catalog content are still in interlaced format. Although deep learning has had huge impact in all forms of image/video processing, learned deinterlacing has not received much attention in the industry or academia. In this paper, we propose a novel multi-field deinterlacing network that aligns features from adjacent fields to a reference field (to be deinterlaced) using deformable residual convolution blocks. To the best of our knowledge, this paper is the first to propose fusion of multi-field features that are aligned via deformable convolutions for deinterlacing. We demonstrate through extensive experimental results that the proposed method provides state-of-the-art deinterlacing results in terms of both PSNR and perceptual quality.

关键词： Industries Deep learning Convolution visual communication image processing Digital TV

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Subjective Quality Assessment of HDR Stereoscopic Omnidirectional Videos

Subjective Quality Assessment of HDR Stereoscopic Omnidirect...

引用

International conference on Communication Technology (ICCT)

作者： Manman Liu Jihao You Ruitao Chen Ziwei Ye Gangyi Jiang Faculty of Information Science and Engineering Ningbo University Ningbo China

High dynamic range stereoscopic omnidirectional video (HSOV) can bring wide field of view and high contrast visual experience to viewers. However, the generation, transmission and display of HSOV will inevitably cause distortions in the video. Therefore, it is important to quantify the impact of the processing of coding and tone mapping on the quality of HSOV. To this end, the subjective quality of HSOV after HEVC coding and tone mapping is evaluated in this paper. Firstly, an HSOV database namely NBU-HSOVD is constructed, which consists of 450 distorted HSOVs. Then, 34 subjects are invited to evaluate the quality of the distorted videos with absolute category rating method so that the MOS value of each video can be provided. Finally, the performance of six existing objective image or video quality assessment methods are tested on NBU-HSOVD. The constructed NBU-HSOVD database can provide reference and basis for the research of objective quality assessment of HSOV in the future.

关键词： visualization image analysis Databases Stereo image processing Distortion Encoding communications technology

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Parallelized Context Modeling for Faster image Coding

Parallelized Context Modeling for Faster Image Coding

引用

IEEE visual communications and image processing (VCIP)

作者： A. Burakhan Koyuncu Kai Cui Atanas Boev Eckehard Steinbach Chair of Media Technology Technical University of Munich Munich Germany Audiovideo Technology Lab Huawei Munich Research Center Munich Germany

ISBN: (纸本)9781728173221

Learning-based image compression has reached the performance of classical methods such as BPG. One common approach is to use an autoencoder network to map the pixel information to a latent space and then approximate the symbol probabilities in that space with a context model. During inference, the learned context model provides symbol probabilities, which are used by the entropy encoder to obtain the bitstream. Currently, the most effective context models use autoregression, but autoregression results in a very high decoding complexity due to the serialized data processing. In this work, we propose a method to parallelize the autoregressive process used for image compression. In our experiments, we achieve a decoding speed that is over 8 times faster than the standard autoregressive context model almost without compression performance reduction.

关键词： image coding visual communication Information sharing Network architecture Data processing Entropy Data models

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GPU-Intensive Fast Entropy Coding Framework for Neural image Compression

GPU-Intensive Fast Entropy Coding Framework for Neural Image...

引用

IEEE visual communications and image processing (VCIP)

作者： Hiroaki Akutsu Takahiro Naruko Akifumi Suzuki Research & Development Group Hitachi Ltd. Japan

ISBN: (纸本)9781728173221

Neural compression has benefited from technological advances such as convolutional neural networks (CNNs) to achieve advanced bitrates, especially in image compression. In neural image compression, an encoder and a decoder can run in parallel on a GPU, so the speed is relatively fast. However, the conventional entropy coding for neural image compression requires serialized iterations in which the probability distribution is estimated by multi-layer CNNs and entropy coding is processed on a CPU. Therefore, the total compression and decompression speed is slow. We propose a fast, practical, GPU-intensive entropy coding framework that consistently executes entropy coding on a GPU through highly parallelized tensor operations, as well as an encoder, decoder, and entropy estimator with an improved network architecture. We experimentally evaluated the speed and rate-distortion performance of the proposed framework and found that we could significantly increase the speed while maintaining the bitrate advantage of neural image compression.

关键词： Multiplexing image coding Tensors visual communication Bit rate Redundancy Graphics processing units

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