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No-Reference VMAF: A Deep Neural Network-based Approach to Blind Video Quality Assessment

IEEE TRANSACTIONS ON BROADCASTING 2024年第3期70卷 844-861页

作者： De Decker, Axel De Cock, Jan Lambert, Peter Van Wallendael, Glenn Univ Ghent Dept Elect & Informat Syst B-9000 Ghent Belgium Synamedia B-8500 Kortrijk Belgium

As the demand for high-quality video content continues to rise, accurately assessing the visual quality of digital videos has become more crucial than ever before. However, evaluating the perceptual quality of an impaired video in the absence of the original reference signal remains a significant challenge. To address this problem, we propose a novel No-Reference (NR) video quality metric called NR-VMAF. Our method is designed to replicate the popular Full-Reference (FR) metric VMAF in scenarios where the reference signal is unavailable or impractical to obtain. Like its FR counterpart, NR-VMAF is tailored specifically for measuring video quality in the presence of compression and scaling artifacts. The proposed model utilizes a deep convolutional neural network to extract quality-aware features from the pixel information of the distorted video, thereby eliminating the need for manual feature engineering. By adopting a patch-based approach, we are able to process high-resolution video data without any information loss. While the current model is trained solely on H.265/HEVC videos, its performance is verified on subjective datasets containing mainly H.264/AVC content. We demonstrate that NR-VMAF outperforms current state-of-the-art NR metrics while achieving a prediction accuracy that is comparable to VMAF and other FR metrics. based on this strong performance, we believe that NR-VMAF is a viable approach to efficient and reliable No-Reference video quality assessment.

关键词： Measurement Streaming media Visualization Feature extraction Quality assessment Video recording Accuracy Video quality assessment no reference compression & scaling artifacts convolutional neural networks patch-based processing

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Efficient and Adaptable patch-based Crack Detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 21885-21896页

作者： Guo, Jing-Ming Markoni, Herleeyandi Natl Taiwan Univ Sci & Technol Dept Elect Engn Adv Intelligent Image & Vis Technol Res Ctr Taipei 10607 Taiwan

Road crack inspection is an important process to maintain the quality of roads for safety issues. The manual road inspection is laborious and time consuming. Thus, an automatic road crack detection is essential to make the inspection process easier and faster. Normally, this crack detection is conducted in real-time with low power computational devices and limited memory. Consequently, many former approaches adopted the patch-based approach to reduce computation for single forward, where the input image is divided into several non-overlapping patches. The generated patches are processed by independent CNN models to capture the crack position. Yet, this approach can lead to disintegrating issues because each patch is processed independently when the CNN fails to detect some patches of the crack. In this study, the improved patch-based crack detector is proposed, and the global patch analyzer is adopted to handle the above issue by considering the relation of each patch processing. Moreover, the proposed model also involves more features such as multiple decoder design and automatic resource mapper to yield superior results than that of the state-of-the-art methods in terms of the speed and accuracy as examined by extensive experiments.

关键词： Decoding Feature extraction Transformers Roads Detectors Convolution Inspection patch-based processing CNN Linformer global patch analyzer automatic controller demanded controller

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Speeding up the patch ordering method for image denoising

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第16期78卷 23639-23657页

作者： Munir, Badre Hussain, Syed Fawad Noor, Adnan GIK Inst Engn Sci & Technol Fac Comp Sci & Engn Topi Pakistan GIK Inst Engn Sci & Technol Machine Learning & Data Sci MDS Lab Topi Pakistan GIK Inst Engn Sci & Technol Fac Elect Engn Topi Pakistan

Smooth ordering of local patches (patch ordering) has been shown to give state-of-the-art results for image denoising. However, use of very large TSPs (Traveling Salesman Problem) makes it computationally intensive. The patch ordering method forms two large TSPs and employs their approximate solutions in a filtering process to perform denoising. On average, 84% of patch ordering's execution time was found to be spent on solving TSPs. A variation of the patch ordering method is proposed with two changes. First, numerous smaller TSPs are formed instead of two large ones. Second, the filtering process is modified to perform denoising with solutions of numerous smaller TSPs instead of two large TSPs. Compared to the patch ordering method, the proposed method can denoise images 3.34 times faster. In terms of PSNR, the proposed method's denoising performance differed by only 0.06 dB on average. Moreover, the proposed method is highly amenable to parallelization. By solving TSPs in parallel, the proposed method's parallel implementation denoised images 4.89 times faster using four CPU cores which reduced denoising time by 80%. Also shown is that given the same computing resources (CPU cores), the proposed method shall attain speedups higher than those by a similarly parallelized version of the patch ordering method. The proposed approach can be used to speed up patch ordering for other image processing tasks.

关键词： Denoising patch-based processing Pixel permutation Traveling salesman Parallel processing Speedup

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Towards a Unified Analysis of Brain Maturation and Aging across the Entire Lifespan: A MRI Analysis

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HUMAN BRAIN MAPPING 2017年第11期38卷 5501-5518页

作者： Coupe, Pierrick Catheline, Gwenaelle Lanuza, Enrique Vicente Manjon, Jose Univ Bordeaux LaBRI UMR 5800 PICTURA F-33400 Talence France CNRS LaBRI UMR 5800 PICTURA F-33400 Talence France Univ Bordeaux EPHE PSL Res Univ CNRS INCIAUMR 5283 F-33000 Bordeaux France Univ Valencia Dept Cell Biol E-46100 Valencia Spain Univ Politecn Valencia Inst Univ Tecnol Informac & Comunicac ITACA Camino Vera S-N E-46022 Valencia Spain

There is no consensus in literature about lifespan brain maturation and senescence, mainly because previous lifespan studies have been performed on restricted age periods and/or with a limited number of scans, making results instable and their comparison very difficult. Moreover, the use of nonharmonized tools and different volumetric measurements lead to a great discrepancy in reported results. Thanks to the new paradigm of BigData sharing in neuroimaging and the last advances in image processing enabling to process baby as well as elderly scans with the same tool, new insights on brain maturation and aging can be obtained. This study presents brain volume trajectory over the entire lifespan using the largest age range to date (from few months of life to elderly) and one of the largest number of subjects (N=2,944). First, we found that white matter trajectory based on absolute and normalized volumes follows an inverted U-shape with a maturation peak around middle life. Second, we found that from 1 to 8-10 y there is an absolute gray matter (GM) increase related to body growth followed by a GM decrease. However, when normalized volumes were considered, GM continuously decreases all along the life. Finally, we found that this observation holds for almost all the considered subcortical structures except for amygdala which is rather stable and hippocampus which exhibits an inverted U-shape with a longer maturation period. By revealing the entire brain trajectory picture, a consensus can be drawn since most of the previously discussed discrepancies can be explained. Hum Brain Mapp 38:5501-5518, 2017. (C) 2017 Wiley Periodicals, Inc.

关键词： aging maturation lifespan MRI segmentation patch-based processing brain trajectory

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Sparse-based Morphometry: Principle and Application to Alzheimer's Disease 1

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2nd International Workshop on patch-based Techniques in Medical Imaging (patchMI)

作者： Coupe, Pierrick Deledalle, Charles-Alban Dossal, Charles Allard, Michele CNRS LaBRI UMR 5800 PICTURA F-33400 Talence France Univ Bordeaux LaBRI UMR 5800 PICTURA F-33400 Talence France Univ Bordeaux IMB UMR 5251 F-33400 Talence France CNRS IMB UMR 5251 F-33400 Talence France Univ Bordeaux INCIA UMR 5287 F-33400 Talence France CNRS INCIA UMR 5287 F-33400 Talence France EPHE Bordeaux France

ISBN: (数字)9783319471181

ISBN: (纸本)9783319471181;9783319471174

The detection of brain alterations is crucial for understanding pathophysiological processes. The Voxel-based Morphometry (VBM) is one of the most popular methods to achieve this task. Despite its numerous advantages, VBM is based on a highly reduced representation of the local brain anatomy since complex anatomical patterns are reduced to local averages of tissue probabilities. In this paper, we propose a new framework called Sparse-based Morphometry (SBM) to better represent local brain anatomies. The presented patch-based approach uses dictionary learning to detect anatomical pattern modifications based on their shape and geometry. In our experiences, we compare SBM and VBM along Alzheimer's Disease (AD) progression.

关键词： Voxel-based morphometry Alzheimer's Disease Dictionary learning Abnormality detection patch-based processing

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Epigraphical projection and proximal tools for solving constrained convex optimization problems

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SIGNAL IMAGE AND VIDEO processing 2015年第8期9卷 1737-1749页

作者： Chierchia, G. Pustelnik, N. Pesquet, J. -C. Pesquet-Popescu, B. Telecom ParisTech CNRS LTCI Inst Mines Telecom F-75014 Paris France ENS Lyon CNRS UMR 5672 Lab Phys F-69007 Lyon France Univ Paris Est LIGM CNRS UMR 8049 F-77454 Marne La Vallee France

We propose a proximal approach to deal with a class of convex variational problems involving nonlinear constraints. A large family of constraints, proven to be effective in the solution of inverse problems, can be expressed as the lower-level set of a sum of convex functions evaluated over different blocks of the linearly transformed signal. For such constraints, the associated projection operator generally does not have a simple form. We circumvent this difficulty by splitting the lower-level set into as many epigraphs as functions involved in the sum. In particular, we focus on constraints involving -norms with , distance functions to a convex set, and -norms with . The proposed approach is validated in the context of image restoration by making use of constraints based on Non-Local Total Variation. Experiments show that our method leads to significant improvements in term of convergence speed over existing algorithms for solving similar constrained problems.

关键词： Iterative methods Optimization Epigraph Projection Proximal algorithms Restoration Total variation Non-local regularization patch-based processing

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A collaborative adaptive Wiener filter for image restoration using a spatial-domain multi-patch correlation model

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EURASIP JOURNAL ON ADVANCES IN SIGNAL processing 2015年第1期2015卷 1-23页

作者： Mohamed, Khaled M. Hardie, Russell C. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

We present a new patch-based image restoration algorithm using an adaptive Wiener filter (AWF) with a novel spatial-domain multi-patch correlation model. The new filter structure is referred to as a collaborative adaptive Wiener filter (CAWF). The CAWF employs a finite size moving window. At each position, the current observation window represents the reference patch. We identify the most similar patches in the image within a given search window about the reference patch. A single-stage weighted sum of all of the pixels in the similar patches is used to estimate the center pixel in the reference patch. The weights are based on a new multi-patch correlation model that takes into account each pixel's spatial distance to the center of its corresponding patch, as well as the intensity vector distances among the similar patches. One key advantage of the CAWF approach, compared with many other patch-based algorithms, is that it can jointly handle blur and noise. Furthermore, it can also readily treat spatially varying signal and noise statistics. To the best of our knowledge, this is the first multi-patch algorithm to use a single spatial-domain weighted sum of all pixels within multiple similar patches to form its estimate and the first to use a spatial-domain multi-patch correlation model to determine the weights. The experimental results presented show that the proposed method delivers high performance in image restoration in a variety of scenarios.

关键词： Image restoration Wiener filter Correlation model patch-based processing

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A Modified Collaborative Adaptive Wiener Filter for Multi-frame Super-resolutionaper

A Modified Collaborative Adaptive Wiener Filter for Multi-fr...

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IEEE National Aerospace and Electronics Conference (NAECON)

作者： Mohamed, Khaled M. Hardie, Russell C. Univ Dayton Dept Elect & Comp Engn Dayton OH 45469 USA

ISBN: (纸本)9781467375658

During acquisition, digital images are invariably degraded by a number of phenomena that limit image resolution and utility. Aliasing from undersampling, blur from optics, and sensor noise are some factors which can affect the image resolution. Multi-frame super-resolution (SR) is a technique that takes several low-resolution (LR) frames of a particular scene and processes them together to produce one or more high-resolution (HR) images. The HR images have higher spatial frequency content, and less noise and blur, than any of the LR frames. A collaborative adaptive Wiener filter (CAWF) for multi-frame SR, proposed by the current authors, is one of the very recent effective multi-frame SR algorithms. In this paper, we modify the original CAWF SR method by employing a spatially varying signal variance estimate. Instead of using a global signal variance estimate as an external input to the original CAWF SR algorithm, we estimate the desired signal variance in each processing window and incorporate it to estimate the HR pixels. The modified CAWF SR is presented and demonstrated. In addition, performance comparisons between the original and the modified CAWF SR are conducted. The modified CAWF SR outperforms the original CAWF SR, particularly in low signal-to-noise ratio images.

关键词： super-resolution collaborative adaptive Wiener filter patch-based processing multi-patch correlation model

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A patch-based Non-local Means Denoising Method Using Hierarchical Searching 1

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10th Chinese Conference on Image and Graphics Technologies and Applications (IGTA)

作者： Dou, Zhi Han, Yubing Sheng, Weixing Ma, Xiaofeng Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China

ISBN: (数字)9783662477915

ISBN: (纸本)9783662477915;9783662477908

Non-local means (NLM) is a powerful denoising algorithm that can protect texture effectively. However, the computational complexity of this method is so high that it is difficult to be widely applied in real-time systems. In this paper, we propose a fast NLM denoising algorithm which can product comparable or better result with less computation time than the traditional NLM methods. Some experimental results are provided to demonstrate the superiority of the proposed method.

关键词： Non-local means patch-based processing Hierarchical searching

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patch-Ordering-based Wavelet Frame and Its Use in Inverse Problems

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IEEE TRANSACTIONS ON IMAGE processing 2014年第7期23卷 2779-2792页

作者： Ram, Idan Cohen, Israel Elad, Michael Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

In our previous work [1] we have introduced a redundant tree-based wavelet transform (RTBWT), originally designed to represent functions defined on high dimensional data clouds and graphs. We have further shown that RTBWT can be used as a highly effective image-adaptive redundant transform that operates on an image using orderings of its overlapped patches. The resulting transform is robust to corruptions in the image, and thus able to efficiently represent the unknown target image even when it is calculated from its corrupted version. In this paper, we utilize this redundant transform as a powerful sparsity-promoting regularizer in inverse problems in image processing. We show that the image representation obtained with this transform is a frame expansion, and derive the analysis and synthesis operators associated with it. We explore the use of this frame operators to image denoising and deblurring, and demonstrate in both these cases state-of-the-art results.

关键词： patch-based processing redundant wavelet frames denoising deblurring ordering regularization

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