Image quality assessment based on wave atoms transform

Haddad, Zehira; Beghdadi, Azeddine; Serir, Amina; Mokraoui, Anissa

doi:10.1109/icip.2010.5653398

Cited by 16 publications

(13 citation statements)

References 14 publications

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“…In [159], Cheng et al supplemented a wavelet-based HVS model with measures of directional structural distortion and structural similarity [123]. Alternative image transforms have also been used to integrate properties of the HVS into IQA algorithms [160,161].…”

Section: Full-reference Image Quality Assessment Algorithmsmentioning

confidence: 99%

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

Chandler

2013

ISRN Signal Processing

375

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: Full-reference Image Quality Assessment Algorithmsmentioning

confidence: 99%

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

Chandler

2013

ISRN Signal Processing

375

191

View full text Add to dashboard Cite

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“…Regarding the issue of feature extraction, several methods/ features have been proposed in literature including local variance and correlation [11], the Singular Value Decomposition [14][15][16], frequency domain transforms like DCT and wavelets [86], wave atoms transform [19], discrete orthogonal transforms [20], contourlet transform [21], Riesz transform [22], etc. In contrast to this, the issue of feature pooling is a relatively less investigated topic.…”

Section: Signal Processing Based Approach For Iqamentioning

confidence: 99%

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Narwaria

Wang

Çetin

2012

Pattern Recognition

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a b s t r a c tMeasurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we first investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effective since they can represent the structural information, which is crucial for IQA. Moreover, they are also beneficial in a reduced-reference scenario where only partial reference image information is used for quality assessment. We address the second issue by exploiting machine learning. In our opinion, the well established methodology of machine learning/pattern recognition has not been adequately used for IQA so far; we believe that it will be an effective tool for feature pooling since the required weights/ parameters can be determined in a more convincing way via training with the ground truth obtained according to subjective scores. This helps to overcome the limitations of the existing pooling methods, which tend to be over simplistic and lack theoretical justification. Therefore, we propose a new metric by formulating IQA as a pattern recognition problem. Extensive experiments conducted using six publicly available image databases (totally 3211 images with diverse distortions) and one video database (with 78 video sequences) demonstrate the effectiveness and efficiency of the proposed metric, in comparison with seven relevant existing metrics.

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“…19 Promising results obtained by DCT-and DWT-based frameworks encourage researchers to develop quality measures that work in the transform domain. [20][21][22] As another feature extraction scheme, Shnayderman et al 23 used singular value decomposition (SVD) to define a quality measure for the images degraded by different levels and different types of distortions. The method computes the distance between the eigenvalues of image subregions on the reference and distorted images.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Image quality assessment using two-dimensional complex mel-cepstrum

Cakir

Çetin

2016

J. Electron. Imaging

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Image quality assessment using two-dimensional complex mel-cepstrum,"Abstract. Assessment of visual quality plays a crucial role in modeling, implementation, and optimization of image-and video-processing applications. The image quality assessment (IQA) techniques basically extract features from the images to generate objective scores. Feature-based IQA methods generally consist of two complementary phases: (1) feature extraction and (2) feature pooling. For feature extraction in the IQA framework, various algorithms have been used and recently, the two-dimensional (2-D) mel-cepstrum (2-DMC) feature extraction scheme has provided promising results in a feature-based IQA framework. However, the 2-DMC feature extraction scheme completely loses image-phase information that may contain high-frequency characteristics and important structural components of the image. In this work, "2-D complex mel-cepstrum" is proposed for feature extraction in an IQA framework. The method tries to integrate Fourier transform phase information into the 2-DMC, which was shown to be an efficient feature extraction scheme for assessment of image quality. Support vector regression is used for feature pooling that provides mapping between the proposed features and the subjective scores. Experimental results show that the proposed technique obtains promising results for the IQA problem by making use of the image-phase information.

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Image quality assessment based on wave atoms transform

Cited by 16 publications

References 14 publications

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

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

Scalable image quality assessment with 2D mel-cepstrum and machine learning approach

Image quality assessment using two-dimensional complex mel-cepstrum

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