&lt;title&gt;Multidimensional image quality measure using singular value decomposition&lt;/title&gt;

Shnayderman, Aleksandr; Gusev, Alexander; Eskicioğlu, Ahmet M.

doi:10.1117/12.530554

Cited by 51 publications

(27 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shnayderman et al [55] developed a distortion measure called M-SVD for image quality assessment based on the concept of singular value decomposition. Singular Value Decomposition is a way of factoring matrices into a series of linear approximations that expose the underlying structure of the matrix.…”

Section: B Natural Visual Characteristicsmentioning

confidence: 99%

Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison

Chikkerur

Sundaram

Reisslein

et al. 2011

IEEE Trans. on Broadcast.

547

272

View full text Add to dashboard Cite

Abstract-With the increasing demand for video-based applications, the reliable prediction of video quality has increased in importance. Numerous video quality assessment methods and metrics have been proposed over the past years with varying computational complexity and accuracy. In this paper, we introduce a classification scheme for full-reference and reduced-reference media-layer objective video quality assessment methods. Our classification scheme first classifies a method according to whether natural visual characteristics or perceptual (human visual system) characteristics are considered. We further subclassify natural visual characteristics methods into methods based on natural visual statistics or natural visual features. We subclassify perceptual characteristics methods into frequency-or pixel-domain methods. According to our classification scheme, we comprehensively review and compare the media-layer objective video quality models for both standard resolution and high definition video. We find that the natural visual statistics based MultiScale-Structural SIMilarity index (MS-SSIM), the natural visual feature based Video Quality Metric (VQM), and the perceptual spatio-temporal frequency-domain based MOtion-based Video Integrity Evaluation (MOVIE) index give the best performance for the LIVE Video Quality Database.Index Terms-Full-reference metric, objective video quality, perceptual video quality, reduced-reference metric.

show abstract

Section: B Natural Visual Characteristicsmentioning

confidence: 99%

Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison

Chikkerur

Sundaram

Reisslein

et al. 2011

IEEE Trans. on Broadcast.

547

272

View full text Add to dashboard Cite

show abstract

“…Recently, the SVD transform was used to measure the image quality under different types of distortions [19].…”

Section: Svd Transformmentioning

confidence: 99%

A Novel Block-based Watermarking Scheme Using the SVD Transform

et al. 2009

View full text Add to dashboard Cite

Abstract:In this paper, a block-based watermarking scheme based on the Singular Value Decomposition (SVD) is proposed. Our watermark, a pseudo-random Gaussian sequence, is embedded by modifying the angles formed by the right singular vectors of each block of the original image. The orthogonality property of the right singular vector matrix is preserved during the embedding process. Several experiments have been carried out to test the performance of the proposed scheme against different attack scenarios. We conclude that the proposed scheme is resistant against common signal processing operations and attacks, while it preserves the quality of the original image.

show abstract

“…A proposed metric using singular value decompositions measures the spectral di¤erences between images [333]. Other recent methods focus on comparing the structure and various components which are preferred by the human visual system, but are not used here due to their lack of quanti…able relationship to the digital imaging and object recognition problem.…”

Section: Minimum Number Of Bitsmentioning

confidence: 99%

“…An area around the copepod was used to estimate the visual image quality using the NMSE (Equation 3.51, or the NRMSE by taking the square root) [116] and a spectral SVD-based metric [333]. (More traditional metrics such as the MSE were also computed for comparison, but discarded as they are dependent on the total energy.…”

Section: Image Degradationmentioning

confidence: 99%

Computational imaging and automated identification for aqueous environments

Loomis¹

2011

View full text Add to dashboard Cite

Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi…cation with bag-of-words models and multi-stage boosting for rock…sh detection. Methods for extracting images of …sh from videos of longline operations are demonstrated.A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the e¤ects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references.Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the e¢ cacy of the prototype and algorithms. Course work at MIT and WHOI was helpful throughout this thesis. In particular, course materials and projects from Dr. Antonio Torralba (computer vision, detection, and boosting), Dr. Frédo Durand (computational imaging, segmentation, and probability), Dr. Rosalind Picard (machine learning, pattern recognition, and probability), Dr. Alan 4 Edelman (parallel computing and algorithms), and Dr. Barbastathis (optical systems) are re ‡ected directly, in several cases extended after the course to become complete sections of this thesis.The MIT Museum has provided unique outreach opportunities for the ideas presented in this thesis. Seth Riskin originally involved our lab in holography activities at the MIT Museum, then Kurt Hasselbalch later proposed and guided the development of an interactive display on plankton holography. The computational approaches I learned for the museum display led to further scienti…c development and spurred the use of GPUs for processing; Section 3.3.5 is a direct consequence, as is the majority of the data processing performed throughout this thesis.In reference to data processing, Matlab/MEX and NVIDIA's CUDA have been daily cornerstones for which I am continually thankful. Finally, mad props go to Dr. José "Pepe" Domínguez-Caballero, the most in ‡uential 5 person in my scienti…c development while at MIT. Pepe taught me digital holography and worked one-on-one with me throughout the beginning of my grad student career. He continued to be involved as a fellow holographer, a mentor, and a friend, discussing ideas, encouraging active experimentation, maintaining a curiosity about optics, and o¤ering eno...

show abstract

<title>Multidimensional image quality measure using singular value decomposition</title>

Cited by 51 publications

References 38 publications

Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison

Objective Video Quality Assessment Methods: A Classification, Review, and Performance Comparison

A Novel Block-based Watermarking Scheme Using the SVD Transform

Computational imaging and automated identification for aqueous environments

Contact Info

Product

Resources

About