APSNR: Artifact Peak Signal-to-Noise Ratio for Image Quality Assessment

An image processing pipeline typically involves key operations like compression, denoising, and resizing, along with enhancements such as sharpening, histogram equalization, and low-light compensation. Within this pipeline, image artifacts are often introduced, which could severely degrade perceptual quality and mislead downstream vision tasks. Yet, current image quality assessment (IQA) models fail to distinguish between harmful artifacts and beneficial enhancements, as they generally apply a rigid fidelity criterion that penalizes all deviations from the reference image. We address this gap with the artifact peak signal-to-noise ratio (APSNR), a new IQA metric that adopts a selective fidelity criterion-allowing legitimate enhancements while penalizing only spurious artifacts. Specifically, APSNR detects artifacts by identifying pixels that violate an "artifact-free" intensity mapping between the processed and reference images, and then computes PSNR exclusively within the artifact-corrupted regions. Extensive experiments demonstrate that our APSNR consistently correlates with human perception of artifacts while remaining robust to enhancements. This enables a more nuanced evaluation of image processing algorithms and provides a principled tool for benchmarking artifact suppression.