Due to the massive popularity of digital images and videos over the past several decades, the need for automated quality assessment (QA) is greater than ever. Accordingly, the impetus on QA research has focused on improving prediction accuracy. However, for many application areas, such as consumer electronics, the runtime performance and related computational considerations are equally as important as the accuracy. Most modern QA algorithms exhibit a large computational complexity. However, the large complexity of these algorithms does not necessarily prohibit their ability of achieving low runtimes if hardware resources are used appropriately. GPUs, which offer a large amount of parallelism and a specialized memory hierarchy, should be well-suited for QA algorithm deployment. In this paper, we analyze a massively parallel GPU implementation of the most apparent distortion (MAD) full-reference image QA algorithm with optimizations guided by a microarchitectural analysis. A shared memory based implementation of the local statistics computation has yielded 25% speedup over its original implementation. We describe the optimizations that produce the best results. We also justify our optimization recommendations with descriptions of the microarchitectural underpinnings. Although our study focuses on a single algorithm, the image-processing primitives used in this algorithm are fundamentally similar to those used in most modern QA algorithms.
Vignesh Kannan, Joshua Holloway, Sohum Sohoni, Damon M. Chandler, "Microarchitectural analysis of a GPU implementation of the Most Apparent Distortion image quality assessment algorithm" in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XIV, 2017, pp 36 - 41, https://doi.org/10.2352/ISSN.2470-1173.2017.12.IQSP-224