Color-Enriched Gradient Similarity for Retouched Image Quality Evaluation

Abstract

SUMMARYImage retouching is fundamental in photography, which is widely used to improve the perceptual quality of a low-quality image. Traditional image quality metrics are designed for degraded images, so they are limited in evaluating the quality of retouched images. This letter presents a RETouched Image QUality Evaluation (RETIQUE) algorithm by measuring structure and color changes between the original and retouched images. Structure changes are measured by gradient similarity. Color colorfulness and saturation are utilized to measure color changes. The overall quality score of a retouched image is computed as the linear combination of gradient similarity and color similarity. The performance of RETIQUE is evaluated on a public Digitally Retouched Image Quality (DRIQ) database. Experimental results demonstrate that the proposed metric outperforms the state-of-the-arts.

A Lightweight Dual-Branch Image Dehazing Network based on Associative Learning

“…Evaluating the performance of an image dehazing algorithm typically involves various evaluation metrics. In this paper, we used Peak Signal-to-Noise Ratio (PSNR) [21] and Structural Similarity Index (SSIM) [22] to measure the reconstruction quality of the images. The PSNR value reflects the difference between the original image and the reconstructed image, where a higher value indicates better image restoration.…”