Performance-analysis-based acceleration of image quality assessment

Two stages are commonly employed in modern algorithms of image/video quality assessment (QA): (1) a local frequency-based decomposition, and (2) block-based statistical comparisons between the frequency coefficients of the reference and distorted images. This paper presents a performance analysis of and techniques for accelerating these stages. We specifically analyze and accelerate one representative QA algorithm recently developed by the authors (Larson and Chandler, 2010). We identify the bottlenecks from the abovementioned stages, and we present methods of acceleration using integral images, inline expansion, a GPGPU implementation, and other code modifications. We show how a combination of these approaches can yield a speedup of 47×.