Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

TL;DR

This paper introduces HVS-MaxPol, a fast and scalable no-reference image sharpness assessment method based on a biologically inspired convolution filter design that correlates well with perceived image blurriness.

Contribution

The paper proposes a novel HVS-inspired convolution filter approach using MaxPol filters for efficient and accurate image sharpness assessment, addressing computational cost and scalability issues.

Findings

HVS-MaxPol achieves high correlation with subjective sharpness scores.

The method is computationally efficient and scalable across different image types.

It outperforms existing NR-ISA metrics in speed and accuracy.

Abstract

In this paper, we propose a novel design of Human Visual System (HVS) response in a convolution filter form to decompose meaningful features that are closely tied with image sharpness level. No-reference (NR) Image sharpness assessment (ISA) techniques have emerged as the standard of image quality assessment in diverse imaging applications. Despite their high correlation with subjective scoring, they are challenging for practical considerations due to high computational cost and lack of scalability across different image blurs. We bridge this gap by synthesizing the HVS response as a linear combination of Finite Impulse Response (FIR) derivative filters to boost the falloff of high band frequency magnitudes in natural imaging paradigm. The numerical implementation of the HVS filter is carried out with MaxPol filter library that can be arbitrarily set for any differential orders and cutoff frequencies to balance out the estimation of informative features and noise sensitivities. We then design an innovative NR-ISA metric called `HVS-MaxPol' that (a) requires minimal computational cost, (b) produce high correlation accuracy with image blurriness, and (c) scales to assess synthetic and natural image blur. Specifically, the synthetic blur images are constructed by blurring the raw images using Gaussian filter, while natural blur is observed from real-life application such as motion, out-of-focus, etc. Furthermore, we create a natural benchmark database in digital pathology for validation of image focus quality in whole slide imaging systems called `FocusPath' consisting of 864 blurred images. Thorough experiments are designed to test and validate the efficiency of HVS-MaxPol across different blur databases and state-of-the-art NR-ISA metrics. The experiment result indicates that our metric has the best overall performance with respect to speed, accuracy and scalability.