Algorithms for image quality assessment (IQA) aim to predict the qualities of images in a manner that agrees with subjective quality ratings. Over the last several decades, the major impetus in IQA research has focused on improving predictive performance; very few studies have focused on analyzing and improving the runtime performance of IQA algorithms. This paper is the first to examine IQA algorithms from the perspective of their interaction with the underlying hardware and microarchitectural resources, and to perform a systematic performance analysis using state-of-the-art tools and techniques from other computing disciplines. We implemented four popular full-reference IQA algorithms (most apparent distortion, multiscale structural similarity, visual information fidelity, and visual signal-to-noise ratio) and two no-reference algorithms (blind image integrity notator using DCT statistics and blind/referenceless image spatial quality evaluator) in C++ based on the code provided by their respective authors. We then conducted a hotspot analysis to identify sections of code that were performance bottlenecks and performed microarchitectural analysis to identify the underlying causes for these bottlenecks. Despite the fact that all six algorithms share common algorithmic operations (e.g., filterbanks and statistical computations), our results revealed that different IQA algorithms overwhelm different microarchitectural resources and give rise to different types of bottlenecks. Based on these results, we propose microarchitectural-conscious coding techniques and custom hardware recommendations for performance improvement.
3-D images obtained from optical sectioning microscopy are usually degraded by a point-spread function that is known to be an even function but is otherwise only approximately known, or even entirely unknown. We present a new algorithm for 3-D blind deconvolution of even point-spread functions that is both fast and (in the absence of noise) exact. Fourier transforms decouple the problem into 2-D, then 1-D blind deconvolution problems, greatly increasing computational speed. Numerical simulations demonstrate that the blind algorithm seems to perform both faster and more accurately than the non-blind iterative Lucy-Richardson algorithm.