원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.9 No.4
2016.04
pp.347-360
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Computation of extent of image visual excellence is of essential importance for many image and video processing appliances, where the objective of quality evaluation algorithms is to automatically evaluate the excellence of images. This paper is the detailed experimental study, classification, analysis and comparison of the subjective non-blind image quality measures. After analysis, evaluation and comparison, these schemes are classified into two groups on the basis of similarity and dissimilarity check. It also scrutinizes the statistical recital of all the quality measures.
목차
Abstract
1. Introduction
2. Mathematical Forms of Image Quality Measures
2.1 L1 Norm:
2.2 Mean Absolute Error [10]:
2.3 Peak Absolute Error:
2.4 Normalized Absolute Error:
2.5 Maximum Difference [10]:
2.6 Square L2 Norm:
2.7 Mean Square Error [9, 10]:
2.8 Root Mean Square Error:
2.9 Peak Mean Square Error [9]:
2.10 Normalized Square Error:
2.11 Normalized Square L2 Norm:
2.12 Signal to Noise Ratio:
2.13 Peak Signal to Noise Ratio [10]:
2.14 Intensity Ratio Variance:
2.15 Chi-Square:
2.16 Paterson Cross Correlation [13]:
2.17 Spearman’s Rank Correlation:
2.18 Minimum Ratio:
2.19 Jaccard Measure [13]:
2.20 Intersection [13]:
2.21 Bhattacharya [13]:
2.22 Contrast:
2.23 Luminance:
2.24 Structural Information:
2.25 Image Fidelity:
2.26 Normalized Cross Correlation [10]:
2.27 Structural Content [10]:
2.28 Average Difference [10]:
2.29 Universal Image Quality Index: [7, 9, 11]
2.30 Structural Similarity Index Measure: [7, 9, 11]
3. Different Types of Classification of Image Quality Measure
3.1 Subjective/Objective Quality Measures [8, 9, 10]
3.2 Blind/Semi-Blind/Non-Blind [8, 9, 10]:
3.3 Based on Type of Information Image Quality Measure (IQM) used [8]:
3.4 Similarity and Dissimilarity Based Classification
4. Experimental Results and Discussion
4.1 Results of Classification of IQMs in Similarity and Dissimilarity Measure:
4.2 Analysis of Statistical Recital
5. Conclusions and Future Work
Acknowledgements
References
1. Introduction
2. Mathematical Forms of Image Quality Measures
2.1 L1 Norm:
2.2 Mean Absolute Error [10]:
2.3 Peak Absolute Error:
2.4 Normalized Absolute Error:
2.5 Maximum Difference [10]:
2.6 Square L2 Norm:
2.7 Mean Square Error [9, 10]:
2.8 Root Mean Square Error:
2.9 Peak Mean Square Error [9]:
2.10 Normalized Square Error:
2.11 Normalized Square L2 Norm:
2.12 Signal to Noise Ratio:
2.13 Peak Signal to Noise Ratio [10]:
2.14 Intensity Ratio Variance:
2.15 Chi-Square:
2.16 Paterson Cross Correlation [13]:
2.17 Spearman’s Rank Correlation:
2.18 Minimum Ratio:
2.19 Jaccard Measure [13]:
2.20 Intersection [13]:
2.21 Bhattacharya [13]:
2.22 Contrast:
2.23 Luminance:
2.24 Structural Information:
2.25 Image Fidelity:
2.26 Normalized Cross Correlation [10]:
2.27 Structural Content [10]:
2.28 Average Difference [10]:
2.29 Universal Image Quality Index: [7, 9, 11]
2.30 Structural Similarity Index Measure: [7, 9, 11]
3. Different Types of Classification of Image Quality Measure
3.1 Subjective/Objective Quality Measures [8, 9, 10]
3.2 Blind/Semi-Blind/Non-Blind [8, 9, 10]:
3.3 Based on Type of Information Image Quality Measure (IQM) used [8]:
3.4 Similarity and Dissimilarity Based Classification
4. Experimental Results and Discussion
4.1 Results of Classification of IQMs in Similarity and Dissimilarity Measure:
4.2 Analysis of Statistical Recital
5. Conclusions and Future Work
Acknowledgements
References
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