No-Reference Quality Prediction of Distorted/Decompressed Images Using ANFIS

De, Indrajit; Sil, Jaya

doi:10.1109/icctd.2009.59

Cited by 2 publications

(2 citation statements)

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“…In (De and Sil, 2009), ANFIS is used to assess quality of distorted/decompressed images without reference to the original image using three statistical features as inputs expressed as linguistic variables, namely area, extent and eccentricity.…”

Section: Related Workmentioning

confidence: 99%

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Image Quality Assessment using ANFIS Approach

El-Alfy

Riaz

2014

Proceedings of the 6th International Conference on Agents and Artificial Intelligence

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Due to the increasing use of digital images in electronic systems, it becomes important to evaluate the degradation in image quality during acquisition, processing, storage and transmission. In this paper, we investigate the ability of the adaptive neuro-fuzzy inference system (ANFIS) for quality assessment of digital images with respect to original (reference) images. Several metrics for objective quality assessment are calculated and used as inputs to an adaptive fuzzy inference system which in turn estimates a differential mean opinion score (DMOS) for different types of distortions. The predicted values are compared with the actual DMOS values using correlation and error measures. With 7-input ANFIS network, the results show that predicted DMOS values are highly correlated to the actual values using a publicly available and subjectively rated image database. For example, for distorted images due to JPEG 2000 compression, the attained results for correlation coefficient, Spearman's ranked correlation, and RMSE are 0.9944, 0.9902, and 3.32, respectively. These results show that combining the advantages of neural networks with fuzzy systems can be a promising approach for predicting the subjective quality of digital images.

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Section: Related Workmentioning

confidence: 99%

“…In a no-reference technique, the assessor has only access to the query image; hence it is also termed as blind assessment, e.g. (De and Sil, 2009;Li et al, 2011). But when the original image is also available, it is termed as full-reference; e.g.…”

Section: Introductionmentioning

confidence: 99%