Decision Fusion for Image Quality Assessment using an Optimization Approach

Oszust, Mariusz

doi:10.1109/lsp.2015.2500819

Cited by 40 publications

(25 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to eliminate the performance bias, random-test is repeated 1000 times and the mean value across these 1000 iterations is reported as the final result in this paper. Table 2 shows the overall performance comparison with state-of-the-art IQA methods including MCSD [1], IFS [4], LCSIM3 [5], CLFE [6], CLR [8], VSI [9], SSIM [20], GMSD [25], ESIM2 [37], and QASD [38] on different databases. For SSIM, an alternative framework based on structural similarity is introduced to evaluate the quality of images.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, an adaptive subdictionaries index is put forward for IQA in [38]. LCSIM3 [5], IFS [4], CLFE [6], CLR [8], ESIM2 [37], and the proposed method try to improve the accuracy and robustness across different databases by multiple feature extraction, indicating that complementary features construction is a promising solution for effective IQA development. However, one of the vital problems for multiple features based IQA methods is to find proper fusion function.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Shang

Zhao

Ding

2018

Journal of Engineering

View full text Add to dashboard Cite

Image quality assessment that aims to evaluate the image quality automatically by a computational model plays a significant role in image processing systems. To meet the need of accuracy and effectiveness, in the proposed method, complementary features including histogram of oriented gradient, edge information, and color information are employed for joint representation of the image quality. Afterwards, the dissimilarities of the extracted features between the distorted and reference images are quantified. Finally, support vector regression is used for distortion indices fusion and objective quality mapping. Experimental results validate that the proposed method outperforms the state-of-the-art methods in terms of consistency with subjective perception and robustness across various databases and different distortion types.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Shang

Zhao

Ding

2018

Journal of Engineering

View full text Add to dashboard Cite

show abstract

“…structural similarity (SSIM) [27] and its modifications, such as MS-SSIM or FSIM [28]. Actually, even the hybrid metrics based on multimetric fusion [29,30] applied for general-purpose IQA produce a single output value, which is easy for the interpretation. Usually, image quality assessment problem is not considered as a typical classification task, when the division into two classes should only be made.…”

Section: Quality Assessment Of 3d Printsmentioning

confidence: 99%

Improved quality assessment of colour surfaces for additive manufacturing based on image entropy

Okarma

Fastowicz

2020

Pattern Anal Applic

View full text Add to dashboard Cite

A reliable automatic visual quality assessment of 3D-printed surfaces is one of the key issues related to computer and machine vision in the Industry 4.0 era. The colour-independent method based on image entropy proposed in the paper makes it possible to detect and identify some typical problems visible on the surfaces of objects obtained by additive manufacturing. Depending on the quality factor, some of such 3D printing failures may be corrected during the printing process or the operation can be aborted to save time and filament. Since the surface quality of 3D-printed objects may be related to some mechanical or physical properties of obtained objects, its fast and reliable evaluation may also be helpful during the quality monitoring procedures. The method presented in the paper utilizes the assumption of the increase of image entropy for irregularly distorted 3D-printed surfaces. Nevertheless, because of the local nature of distortions, the direct application of the global entropy does not lead to satisfactory results of automatic surface quality assessment. Therefore, the extended method, based on the combination of the local image entropy and its variance with additional colour adjustment, is proposed in the paper, leading to the proper classification of 78 samples used during the experimental verification of the proposed approach.

show abstract

“…In [21,22], scores of MSSIM, VIF and R-SVD were non-linearly combined. A preliminary work with non-linear combination of several IQA measures selected by a genetic algorithm was shown in [23]. In [17], SNR, SSIM, VIF, and VSNR were combined using canonical correlation analysis, and a regularised regression was used to combine up to seven IQA models in [13].…”

Section: Introductionmentioning

confidence: 99%

Image quality assessment with lasso regression and pairwise score differences

Oszust

2016

Multimed Tools Appl

Self Cite

View full text Add to dashboard Cite

The reception of multimedia applications often depends on the quality of processed and displayed visual content. This is the main reason for the development of automatic image quality assessment (IQA) techniques which try to mimic properties of human visual system and produce objective scores for evaluated images. Most of them require a training step in which subjective scores, obtained in tests with human subjects, are used for parameters tuning. In this paper, it is shown that pairwise score differences (PSD) can be successfully used for training a full-reference hybrid IQA measure based on the least absolute shrinkage and selection operator (lasso) regression. The results of extensive experimental evaluation on four largest IQA benchmarks show that the proposed IQA technique is statistically better than its version trained using raw scores, and both approaches are statistically better than state-of-the-art full-reference IQA measures. They are also better than other hybrid approaches. In the paper, the evaluation protocol is extended with tests using PSD.

show abstract

Decision Fusion for Image Quality Assessment using an Optimization Approach

Cited by 40 publications

References 35 publications

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Improved quality assessment of colour surfaces for additive manufacturing based on image entropy

Image quality assessment with lasso regression and pairwise score differences

Contact Info

Product

Resources

About