Objective quality is video quality as computed by an algorithm that reads the pixels or the bitstream and outputs a number, with no humans in the loop. Common metrics are PSNR, which reports raw pixel error in decibels, SSIM, which compares image structure on a 0-to-1 scale, and VMAF, which fuses several measures into a perception-trained 0-to-100 score. Their appeal is that they are fast, deterministic, and repeatable, so they scale to thousands of encodes and power quality gates, regression tests, per-title encoding, and live monitoring. The catch is that an objective score is a model of human opinion, not opinion itself: it is only as trustworthy as its last validation against subjective Mean Opinion Scores, and it can fail on grain, banding, text, dark or high-motion scenes, or footage sharpened to game the number. Objective quality is the scalable proxy for subjective quality, the ground truth it tries to predict.
