About: Video processing is a(n) research topic. Over the lifetime, 25804 publication(s) have been published within this topic receiving 374555 citation(s).
19 Dec 2003-
Abstract: About the Author.Foreword.Preface.Glossary.1. Introduction.2. Video Formats and Quality.3. Video Coding Concepts.4. The MPEG-4 and H.264 Standards.5. MPEG-4 Visual.6. H.264/MPEG-4 Part 10.7. Design and Performance.8. Applications and Directions.Bibliography.Index.
01 Nov 2006-IEEE Transactions on Image Processing
TL;DR: This paper presents results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects and is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image.
Abstract: Measurement of visual quality is of fundamental importance for numerous image and video processing applications, where the goal of quality assessment (QA) algorithms is to automatically assess the quality of images or videos in agreement with human quality judgments. Over the years, many researchers have taken different approaches to the problem and have contributed significant research in this area and claim to have made progress in their respective domains. It is important to evaluate the performance of these algorithms in a comparative setting and analyze the strengths and weaknesses of these methods. In this paper, we present results of an extensive subjective quality assessment study in which a total of 779 distorted images were evaluated by about two dozen human subjects. The "ground truth" image quality data obtained from about 25 000 individual human quality judgments is used to evaluate the performance of several prominent full-reference image quality assessment algorithms. To the best of our knowledge, apart from video quality studies conducted by the Video Quality Experts Group, the study presented in this paper is the largest subjective image quality study in the literature in terms of number of images, distortion types, and number of human judgments per image. Moreover, we have made the data from the study freely available to the research community . This would allow other researchers to easily report comparative results in the future
01 Jan 2000-
TL;DR: The Handbook of Image and Video Processing contains a comprehensive and highly accessible presentation of all essential mathematics, techniques, and algorithms for every type of image and video processing used by scientists and engineers.
Abstract: 1.0 INTRODUCTION 1.1 Introduction to Image and Video Processing (Bovik) 2.0 BASIC IMAGE PROCESSING TECHNIQUES 2.1 Basic Gray-Level Image Processing (Bovik) 2.2 Basic Binary Image Processing (Desai/Bovik) 2.3 Basic Image Fourier Analysis and Convolution (Bovik) 3.0 IMAGE AND VIDEO PROCESSING Image and Video Enhancement and Restoration 3.1 Basic Linear Filtering for Image Enhancement (Acton/Bovik) 3.2 Nonlinear Filtering for Image Enhancement (Arce) 3.3 Morphological Filtering for Image Enhancement and Detection (Maragos) 3.4 Wavelet Denoising for Image Enhancement (Wei) 3.5 Basic Methods for Image Restoration and Identification (Biemond) 3.6 Regularization for Image Restoration and Reconstruction (Karl) 3.7 Multi-Channel Image Recovery (Galatsanos) 3.8 Multi-Frame Image Restoration (Schulz) 3.9 Iterative Image Restoration (Katsaggelos) 3.10 Motion Detection and Estimation (Konrad) 3.11 Video Enhancement and Restoration (Lagendijk) Reconstruction from Multiple Images 3.12 3-D Shape Reconstruction from Multiple Views (Aggarwal) 3.13 Image Stabilization and Mosaicking (Chellappa) 4.0 IMAGE AND VIDEO ANALYSIS Image Representations and Image Models 4.1 Computational Models of Early Human Vision (Cormack) 4.2 Multiscale Image Decomposition and Wavelets (Moulin) 4.3 Random Field Models (Zhang) 4.4 Modulation Models (Havlicek) 4.5 Image Noise Models (Boncelet) 4.6 Color and Multispectral Representations (Trussell) Image and Video Classification and Segmentation 4.7 Statistical Methods (Lakshmanan) 4.8 Multi-Band Techniques for Texture Classification and Segmentation (Manjunath) 4.9 Video Segmentation (Tekalp) 4.10 Adaptive and Neural Methods for Image Segmentation (Ghosh) Edge and Boundary Detection in Images 4.11 Gradient and Laplacian-Type Edge Detectors (Rodriguez) 4.12 Diffusion-Based Edge Detectors (Acton) Algorithms for Image Processing 4.13 Software for Image and Video Processing (Evans) 5.0 IMAGE COMPRESSION 5.1 Lossless Coding (Karam) 5.2 Block Truncation Coding (Delp) 5.3 Vector Quantization (Smith) 5.4 Wavelet Image Compression (Ramchandran) 5.5 The JPEG Lossy Standard (Ansari) 5.6 The JPEG Lossless Standard (Memon) 5.7 Multispectral Image Coding (Bouman) 6.0 VIDEO COMPRESSION 6.1 Basic Concepts and Techniques of Video Coding (Barnett/Bovik) 6.2 Spatiotemporal Subband/Wavelet Video Compression (Woods) 6.3 Object-Based Video Coding (Kunt) 6.4 MPEG-I and MPEG-II Video Standards (Ming-Ting Sun) 6.5 Emerging MPEG Standards: MPEG-IV and MPEG-VII (Kossentini) 7.0 IMAGE AND VIDEO ACQUISITION 7.1 Image Scanning, Sampling, and Interpolation (Allebach) 7.2 Video Sampling and Interpolation (Dubois) 8.0 IMAGE AND VIDEO RENDERING AND ASSESSMENT 8.1 Image Quantization, Halftoning, and Printing (Wong) 8.2 Perceptual Criteria for Image Quality Evaluation (Pappas) 9.0 IMAGE AND VIDEO STORAGE, RETRIEVAL AND COMMUNICATION 9.1 Image and Video Indexing and Retrieval (Tsuhan Chen) 9.2 A Unified Framework for Video Browsing and Retrieval (Huang) 9.3 Image and Video Communication Networks (Schonfeld) 9.4 Image Watermarking (Pitas) 10.0 APPLICATIONS OF IMAGE PROCESSING 10.1 Synthetic Aperture Radar Imaging (Goodman/Carrera) 10.2 Computed Tomography (Leahy) 10.3 Cardiac Imaging (Higgins) 10.4 Computer-Aided Detection for Screening Mammography (Bowyer) 10.5 Fingerprint Classification and Matching (Jain) 10.6 Probabilistic Models for Face Recognition (Pentland/Moghaddam) 10.7 Confocal Microscopy (Merchant/Bartels) 10.8 Automatic Target Recognition (Miller) Index
A. Murat Tekalp1•Institutions (1)
01 Aug 1995-
TL;DR: Digital Video Processing, Second Edition, reflects important advances in image processing, computer vision, and video compression, including new applications such as digital cinema, ultra-high-resolution video, and 3D video.
Abstract: Over the years, thousands of engineering students and professionals relied on Digital Video Processing as the definitive, in-depth guide to digital image and video processing technology. Now, Dr. A. Murat Tekalp has completely revamped the first edition to reflect todays technologies, techniques, algorithms, and trends. Digital Video Processing, Second Edition, reflects important advances in image processing, computer vision, and video compression, including new applications such as digital cinema, ultra-high-resolution video, and 3D video. This edition offers rigorous, comprehensive, balanced, and quantitative coverage of image filtering, motion estimation, tracking, segmentation, video filtering, and compression. Now organized and presented as a true tutorial, it contains updated problem sets and new MATLAB projects in every chapter. Coverage includes Multi-dimensional signals/systems: transforms, sampling, and lattice conversion Digital images and video: human vision, analog/digital video, and video quality Image filtering: gradient estimation, edge detection, scaling, multi-resolution representations, enhancement, de-noising, and restoration Motion estimation: image formation; motion models; differential, matching, optimization, and transform-domain methods; and 3D motion and shape estimation Video segmentation: color and motion segmentation, change detection, shot boundary detection, video matting, video tracking, and performance evaluation Multi-frame filtering: motion-compensated filtering, multi-frame standards conversion, multi-frame noise filtering, restoration, and super-resolution Image compression: lossless compression, JPEG, wavelets, and JPEG2000 Video compression: early standards, ITU-T H.264/MPEG-4 AVC, HEVC, Scalable Video Compression, and stereo/multi-view approaches
TL;DR: The purpose of this article is to provide a systematic classification of various ideas and techniques proposed towards the effective abstraction of video contents, and identify and detail, for each approach, the underlying components and how they are addressed in specific works.
Abstract: The demand for various multimedia applications is rapidly increasing due to the recent advance in the computing and network infrastructure, together with the widespread use of digital video technology. Among the key elements for the success of these applications is how to effectively and efficiently manage and store a huge amount of audio visual information, while at the same time providing user-friendly access to the stored data. This has fueled a quickly evolving research area known as video abstraction. As the name implies, video abstraction is a mechanism for generating a short summary of a video, which can either be a sequence of stationary images (keyframes) or moving images (video skims). In terms of browsing and navigation, a good video abstract will enable the user to gain maximum information about the target video sequence in a specified time constraint or sufficient information in the minimum time. Over past years, various ideas and techniques have been proposed towards the effective abstraction of video contents. The purpose of this article is to provide a systematic classification of these works. We identify and detail, for each approach, the underlying components and how they are addressed in specific works.