Multi-Exposure Image Fusion: Perceptual Quality Assessment & Metrics

Perceptual quality assessment for multi-exposure image fusion refers to the evaluation of the visual quality of images that have been fused from multiple exposures. This process is typically used to create images that have a greater dynamic range than can be captured in a single exposure.

There are several factors that can affect the perceptual quality of multi-exposure image fusions, including the alignment of the different exposures, the choice of fusion algorithm, and the presence of artifacts such as ghosting or noise.

To assess the perceptual quality of multi-exposure image fusions, a range of subjective and objective metrics can be used. Subjective metrics involve human observers who evaluate the images based on their visual quality, while objective metrics use mathematical algorithms to measure various image quality parameters such as contrast, sharpness, and color accuracy.

Subjective metrics include methods such as ACR (Absolute Category Rating) and DMOS (Difference Mean Opinion Score), which ask human observers to rate the visual quality of the images on a scale. Objective metrics include methods such as PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index), which compare the fused image to the original exposures and measure the degree of distortion introduced during the fusion process.

Overall, a combination of subjective and objective metrics can provide a more comprehensive evaluation of the perceptual quality of multi-exposure image fusions, helping to ensure that the resulting images meet the desired quality standards.