Abstract
This study proposes a no-reference image quality assessment method for blurred images. In this approach, first, a discrete wavelet transform was applied to the sample images and then the results were decomposed into four subbands. This was followed by the calculation of the spatial frequencies of high-high (HH2) and low-low (LL2) subbands. Then the ratio of spatial frequencies of HH2 and LL2 subbands was calculated. Information about the image quality was obtained by using this ratio, with lower values indicative of better image quality. The study aims to investigate whether the proposed method is capable of measuring the image quality. The proposed technique was tested on the standard images. Three different images were used, of which each one was distorted with the same type and amount of noise. Motion noise, blurring and sharpening was applied to distort the images. The performance of the proposed method was evaluated and compared with eight representative image quality measures. This provides a meaningful comparison across different types of image distortions. Then, the cameraman image was also blurred with two different noises: Gaussian and disk-shaped blur. The varying amount of blur was compared with Universal Image Quality Index (UIQI) values of the cameraman image. The method gives good results in different resolutions as well. Its computation is easy, independent of viewing conditions.
Highlights
IMAGE quality is a characteristic of an image that measures the detected image corruption
The test images used for our analysis shown were in
It is hard to get the same quality score even if the same level of distortion is applied to different images
Summary
IMAGE quality is a characteristic of an image that measures the detected image corruption. In FR metrics, the quality of a test image is measured by comparing it with a reference image. The MSE (Mean Squared Error) and the PSNR (Peak Signal-to-Noise Ratio) are the two most common objective assessment methods [3]. The MSE, SNR and PSNR are computed quickly These assessment methods are not always the best choice, especially if a comparison will be performed against the human perception of the image quality [4]. A mathematically defined universal image quality index is proposed [5] This approach does not depend on the images being tested, the individual observers or the viewing conditions. A no reference blur image quality metric was introduced in [17] This approach was based on wavelet transform. This study proposes a no-reference image quality assessment method for blurred images. The achievement of the proposed method was estimated and compared with the other representative quality assessment
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