image quality assessment
What is it about?
Image quality assessment (IQA) is the process of evaluating the perceived quality of an image, based on different criteria such as sharpness, color accuracy, noise level, contrast, brightness, and visual artifacts. IQA can be subjective or objective, depending on whether it involves human perception or machine measurements. Subjective IQA methods rely on human observers to rate the quality of images. These methods typically involve presenting a series of images to a group of people, who then rate the images according to their perceived quality. The ratings are then averaged to produce a quality score for each image. Objective IQA methods, on the other hand, use computational algorithms to measure the quality of images automatically. These algorithms typically analyze the image data to identify and measure various image features, such as sharpness, contrast, and color accuracy, and then use mathematical models to calculate an overall quality score for the image. Objective IQA methods are generally more consistent and reproducible than subjective methods, but they may not always reflect human perception accurately. Therefore, a combination of subjective and objective methods is often used for image quality assessment in practical applications.
Why is it important?
To determine whether an image quality is good or not, there are several rules or metrics that can be used. Here are some commonly used metrics for image quality assessment: Peak Signal-to-Noise Ratio (PSNR): This is a commonly used metric that measures the difference between the original and the compressed image. It measures the ratio of the peak signal power to the noise power, and the higher the PSNR, the better the quality of the image. Structural Similarity Index (SSIM): This is another popular metric that compares the structural similarity between the original and the compressed image. The SSIM score ranges from -1 to 1, with 1 indicating perfect similarity and -1 indicating no similarity. Mean Squared Error (MSE): This measures the average squared difference between the original and the compressed image. A lower MSE value indicates a better quality image. Visual Information Fidelity (VIF): This metric measures the quality of the image based on how well it preserves the visual information in the original image. Multi-Scale Structural Similarity Index (MS-SSIM): This metric is an extension of SSIM and takes into account the differences in structural information at different scales of the image. These metrics can be combined and used in different ways depending on the specific requirements of the application. For example, in some applications, a high PSNR might be preferred, while in others, a high SSIM might be more important.