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Blind image sharpness assessment based on local contrast map statistics
This paper presents a fast blind image sharpness/blurriness assessment model (BISHARP) which operates in spatial and transform domain. The proposed model generates local contrast image maps by computing the root-mean-squared values for each image pixel within a defined size of local neighborhood. The resulting local contrast maps are then transformed into the wavelet domain where the reduction of high frequency content is evaluated in the presence of varying blur strengths. It was found that percentile values computed from sorted, level-shifted, high-frequency wavelet coefficients can serve as reliable image sharpness/blurriness estimators. Furthermore, it was found that higher dynamic range of contrast maps significantly improves model performance. The results of validation performed on seven image databases showed a very high correlation with perceptual scores. Due to low computational requirements the proposed model can be easily utilized in real-world image processing applications.
The proposed BISHARP model incorporates processing in the spatial and wavelet transform domain. The flowchart of the proposed model is shown in Fig. 1. It is a fast and straightforward process where an image being tested for sharpness is first converted to grayscale domain. Then, the local contrast map is generated computing the root mean square values in local pixel neighborhood. The generated map is transformed to frequency domain using one-scale discrete wavelet transform. Extracted sub-band coefficients are sorted and level-shifted by the maximum value found in a negative valued wavelet coefficients pool. The computed percentile value of the resulting, level-shifted wavelet coefficients represents the final image sharpness score.
Fig. 1 Flowchart of the BISHARP model
BISHARP release agreement
The BISHARP algorithm can be used under the following terms:
- All documents and papers that report research results obtained using the BISHARP will acknowledge the use of the BISHARP measure.
Please cite the following paper:
G. Gvozden, S. Grgic, and M. Grgic, “Blind image sharpness assessment based on local contrast map statistics,” Journal of Visual Communication and Image Representation, vol. 50, 2018, pp. 145–158.
The article is available at ScienceDirect (hyperlink - https://www.sciencedirect.com/science/article/pii/S1047320317302262) - While every effort has been made to ensure accuracy, BISHARP owners cannot accept responsibility for errors or omissions.
- Use of BISHARP measure is free of charge.
- BISHARP owners reserve the right to revise, amend, alter or delete the information provided herein at any time, but shall not be responsible for or liable in respect of any such revisions, amendments, alterations or deletions.