Reduced-reference image quality assessment based on distortion families of local perceived sharpness

Zhang, Yi; Phan, Thien D.; Chandler, Damon M.

doi:10.1016/j.image.2017.03.020

Cited by 21 publications

(8 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The assessment methods of image sharpness are divided into three types: full reference type, semi-reference type and non-reference type [19,20,21]. The nonreference sharpness assessment method is to assess an indicator of an image to be evaluated, such as the degree of blurring, without using the original image as a reference [21].…”

Section: Sharpness Assessment Of Hologramsmentioning

confidence: 99%

Study on Holographic Image Recognition Technology of Zooplankton

Shi¹,

Wang²,

Cao³

et al. 2019

dtcse

View full text Add to dashboard Cite

Marine zooplankton has important ecological and economic value. The observation and automatic image recognition technology of marine zooplankton is an important mean to acquire data such as species, quantity, spatial distribution and behavioral postures of zooplankton, and is an important support for marine scientific research. Digital holography has an innate advantage of refocusing and reconstruction, which is suitable for deep learning and living zooplankton recognition. In this study, a large number of holographic images was trained by using the improved YOLOv2 model, and after test, the study achieved satisfactory results: the models trained by the images with sharpness assessment score of 0.6 or higher, have precision rate above 94% and a recall rate above 88%. This study mainly discusses: (1) the detection method of moving targets to acquire the images of moving zooplankton; (2) the two factors that affect the holographic images recognition results, mean (pixel mean of images) subtraction operation and image sharpness, and the no-reference sharpness assessment based on structural similarity for holographic images; (3) the relationship between sharpness assessment index or mean subtraction and the recognition results.

show abstract

Section: Sharpness Assessment Of Hologramsmentioning

confidence: 99%

Study on Holographic Image Recognition Technology of Zooplankton

Shi¹,

Wang²,

Cao³

et al. 2019

dtcse

View full text Add to dashboard Cite

show abstract

“…Next step is to calculate the weights for each of the partial quality scores (PQSs) which are the scores coming from each percentile based on (10). We simply consider the median of the values in each percentile as the weight factor for the corresponding PQS.…”

Section: The Sparseness Significance Ranking Measure (Ssrm)mentioning

confidence: 99%

“…Some frameworks specifically address the colour perception characteristics of the HVS [9]. Apart from Full-Reference methods, a number of Reduced Reference (RR) quality measures have been recently introduced, among them some prominent examples [10]- [14]. These methods essentially provide quality judgement based on a limited set of features representing the general image characteristics in combination with a sensitivity to a particular -often application dependent -set of distortion types.…”

Section: Introductionmentioning

confidence: 99%

From Sparse Coding Significance to Perceptual Quality: A New Approach for Image Quality Assessment

Ahar

Barri

Schelkens

2018

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

An increasing number of image processing applications require an automated quality prediction of the visual content as perceived by humans. Since, sparse coding is suggested to be an underlying strategy of the brain's neural system, it would be logical to assume that specific tasks like quality assessment also attempt to adhere to this strategy. However, existing perceptual quality predictors, often mimicking the different stages of the human visual system and deploying machine learning strategies, such as neural networks, rarely integrate the concept of sparse coding in their design. In this paper, we first investigate the validity of such assumption by performing an empirical analysis on the relation between the structural information of the scene-captured via sparseness significance-and perceptual quality. Subsequently, we propose a new approach to integrate the significance of sparse coding features in the future imagequality measure (IQM) designs. We utilize the Fourier transform as a case study, which leads to a new IQM called sparseness significance ranking measure (SSRM). This measure essentially deploys a Fourier basis for sparse coding, a ranking mechanism based upon the amplitudes of the sparse coefficients and subsequently a complex correlation metric that assesses the correspondence between the ranked coefficient amplitude profiles of the reference and the distorted image. Moreover, we introduce a new methodology, namely separation ratio analysis, to assess the prediction quality of individual features or quality predictors given a target perceptual quality. The quality predictions by the proposed SSRM show excellent compatibility with perceptual quality scores. A set of routine benchmarking experiments utilizing the LIVE and CSIQ, IVC and TID2008 databases indicates a highly competitive performance with state of the art IQMs. Moreover, it delivers this performance at a low computational cost.

show abstract

“…FR-IQA techniques require the pristine version of the image to predict the quality score of images [2][3][4][5][6][7][8][9][10]. RR-IQA techniques *Correspondence: 12phdnizami@seecs.edu.pk 1 School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan Full list of author information is available at the end of the article do not require the whole reference image but some information extracted from the reference image to perform IQA [11][12][13][14][15][16][17]. Techniques that evaluate the image quality score without the use of any prior information of reference image are called blind image quality assessment (BIQA) techniques [1].…”

Section: Introductionmentioning

confidence: 99%

Distortion-specific feature selection algorithm for universal blind image quality assessment

Nizami

Majid

Manzoor

et al. 2019

J Image Video Proc.

View full text Add to dashboard Cite

Blind image quality assessment (BIQA) aims to use objective measures for predicting the quality score of distorted images without any prior information regarding the reference image. Several BIQA techniques are proposed in literature that use a two-step approach, i.e., feature extraction for distortion classification and regression for predicting the quality score. In this paper, a three-step approach is proposed that aims to improve the performance of BIQA techniques. In the first step, feature extraction is performed using existing BIQA techniques to determine the distortion type. Secondly, features are selected for each distortion type based on the mean value of Spearman rank ordered correlation constant (SROCC) and linear correlation constant (LCC). Lastly, distortion-specific features are used by regression model to predict the quality score. Experimental results show that the predicted quality score using distortion-specific features strongly correlates with the subjective quality score, improves the overall performance of existing BIQA techniques, and reduces the processing time.

show abstract

Reduced-reference image quality assessment based on distortion families of local perceived sharpness

Cited by 21 publications

References 42 publications

Study on Holographic Image Recognition Technology of Zooplankton

Study on Holographic Image Recognition Technology of Zooplankton

From Sparse Coding Significance to Perceptual Quality: A New Approach for Image Quality Assessment

Distortion-specific feature selection algorithm for universal blind image quality assessment

Contact Info

Product

Resources

About