Abstract
No-reference (NR) image quality assessment (IQA) objectively measures the image quality consistently with subjective evaluations by using only the distorted image. In this paper, we focus on the problem of NR IQA for blurred images and propose a new no-reference structural similarity (NSSIM) metric based on re-blur theory and structural similarity index (SSIM). We extract blurriness features and define image blurriness by grayscale distribution. NSSIM scores an image quality by calculating image luminance, contrast, structure and blurriness. The proposed NSSIM metric can evaluate image quality immediately without prior training or learning. Experimental results on four popular datasets show that the proposed metric outperforms SSIM and well-matched to state-of-the-art NR IQA models. Furthermore, we apply NSSIM with known IQA approaches to blurred image restoration and demonstrate that NSSIM is statistically superior to peak signal-to-noise ratio (PSNR), SSIM and consistent with the state-of-the-art NR IQA models.
Highlights
Advances in digital techniques enable people to capture, store and send a large amount of digital images which sharply accelerates the rate of information transfer
The other is that we extend the famous FR image quality assessment (IQA) metric structural similarity index (SSIM) to a no-reference manner, achieving state-of-the-art IQA performance without previous training
We focus on blurred IQA
Summary
Advances in digital techniques enable people to capture, store and send a large amount of digital images which sharply accelerates the rate of information transfer. Image acquisition and processing systems in real applications need image quality assessment (IQA) to objectively and automatically identify and quantify these image quality degradations. We propose an NR IQA method for blurred images. Our NSSIM can be computed using the input image and its re-blurred image, and no previous training is needed. We apply NSSIM to evaluate the performance of the blurred image restoration. The other is that we extend the famous FR IQA metric SSIM to a no-reference manner, achieving state-of-the-art IQA performance without previous training.
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