Perceptual quality evaluation of synthetic pictures distorted by compression and transmission.

Measuring visual quality, as perceived by human observers, is becoming increasingly important in a large number of applications where humans are the ultimate consumers of visual information. Many natural image databases have been developed that contain human subjective ratings of the images. Subjective quality evaluation data is less available for synthetic images, such as those commonly encountered in graphics novels, online games or internet ads. A wide variety of powerful full-reference, reduced-reference and no-reference Image Quality Assessment (IQA) algorithms have been proposed for natural images, but their performance has not been evaluated on synthetic images. In this paper we (1) conduct a series of subjective tests on a new publicly available Embedded Signal Processing Laboratory (ESPL) Synthetic Image Database, which contains 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 resolution, and (2) evaluate the performance of more than 50 publicly available IQA algorithms on the new database. The synthetic images in the database were processed by post acquisition distortions, including those arising from compression and transmission. We collected 26,000 individual ratings from 64 human subjects which can be used to evaluate full-reference, reduced-reference, and no-reference IQA algorithm performance. We find that IQA models based on scene statistics models can successfully predict the perceptual quality of synthetic scenes. The database is available at: http://signal.ece.utexas.edu/%7Ebevans/synthetic/.