Reduced reference image quality assessment metric using optimized parameterized wavelet watermarking

Avanaki, Alireza Nasiri; Sodagari, Shabnam; Diyanat, Abolfazl

doi:10.1109/icosp.2008.4697266

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…In [287], Avanaki et al presented an RR algorithm which operates by using watermarking to embed RR features into an image; these features can be extracted and used for IQA in the event that the image is distorted. The RR features used in [287] consist of approximation coefficients of a parameterized DWT of the image. At the receiver, the embedded features are extracted and compared to the corresponding features of the distorted image to estimate quality.…”

Section: Reduced-reference Iqamentioning

confidence: 99%

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Chandler

2013

ISRN Signal Processing

374

191

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Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.

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Section: Reduced-reference Iqamentioning

confidence: 99%

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Chandler

2013

ISRN Signal Processing

374

191

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“…Ranges of wavelets are usually needed to examine the data. Wavelet-based watermarking schemes presented in [77][78][79] is the approximation of a parameterized Discrete Wavelet Transform (DWT). At the transmitter side, original image features are embedded in the original multimedia contents via a robust watermarking wavelet technique.…”

Section: Discrete Wavelet Transform Coefficient-based Methodsmentioning

confidence: 99%

Reduced reference image and video quality assessments: review of methods

Dost

Saud

Shabbir

et al. 2022

J Image Video Proc.

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With the growing demand for image and video-based applications, the requirements of consistent quality assessment metrics of image and video have increased. Different approaches have been proposed in the literature to estimate the perceptual quality of images and videos. These approaches can be divided into three main categories; full reference (FR), reduced reference (RR) and no-reference (NR). In RR methods, instead of providing the original image or video as a reference, we need to provide certain features (i.e., texture, edges, etc.) of the original image or video for quality assessment. During the last decade, RR-based quality assessment has been a popular research area for a variety of applications such as social media, online games, and video streaming. In this paper, we present review and classification of the latest research work on RR-based image and video quality assessment. We have also summarized different databases used in the field of 2D and 3D image and video quality assessment. This paper would be helpful for specialists and researchers to stay well-informed about recent progress of RR-based image and video quality assessment. The review and classification presented in this paper will also be useful to gain understanding of multimedia quality assessment and state-of-the-art approaches used for the analysis. In addition, it will help the reader select appropriate quality assessment methods and parameters for their respective applications.

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“…According to Li and Wang [82], despite the choice of the features for the reference image is flexible, it must satisfy 3 conditions: 1) provide a good summary of the reference image; 2) be sensitive to several image distortions; and 3) be relevant to the visual perception of image quality. Examples of RR methods include an adapted version of SSIM [109] and the use of DWT (Discrete Wavelet Transform) coefficients for the set of features of the reference images [94,121,133] • No-Reference (NR) or Blind-Reference: as the name suggests, NR methods do not need reference images in order to measure image quality. Instead, they look for specific distortions in images, such as blurring [42], sharpness [146], blocking [25], ringing [85] or other types of image noise.…”

Section: Image Quality Assessment (Iqa)mentioning

confidence: 99%

Strategies for development and evaluation of video summarization algorithms

Cirne¹

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Sumarização de vídeos é uma área bastante popular que consiste em gerar uma sinopse de um dado vídeo contendo os eventos mais importantes. Ela também é aplicada na análise de grandes quantidades de vídeos digitais, sendo útil na aceleração das tarefas de indexação, busca e recuperação por conteúdo. No entanto, devido ao fato de que existem vários gêneros de conteúdo de vídeos, que incluem esportes documentários, noticiários, programas de TV, entre outros, definir se um evento é ou não importante é um problema em aberto. Várias abordagens têm sido propostas para lidar com esse problema, as quais podem ser ou voltadas para um domínio específico, trabalhando somente com vídeos de um dado gênero, ou de propósito geral, trabalhando com qualquer tipo de vídeo independente do seu gênero. Além disso, a avaliação de métodos de sumarização de vídeos não é uma tarefa trivial, uma vez que isso depende fortemente de fatores subjetivos, normalmente baseados em uma média de opiniões de usuários, para determinar a qualidade dos resumos gerados, o que também se torna um fardo ao comparar um método específico contra outros. Este trabalho discute algumas das estratégias utilizadas para sumarização de vídeos e apresenta três diferentes abordagens que lidam com vídeos de qualquer gênero, juntamente com suas respectivas métricas de avaliação e as evoluções entre essas abordagens. A comparação dos resultados obtidos com outros métodos mostra que foi possível desenvolver um método que não apenas possui uma qualidade superior em relação ao estado da arte mas também é rápido e eficiente, sendo aplicável em ambientes de gerenciamento de vídeos.

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Reduced reference image quality assessment metric using optimized parameterized wavelet watermarking

Cited by 3 publications

References 13 publications

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Reduced reference image and video quality assessments: review of methods

Strategies for development and evaluation of video summarization algorithms

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