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.

本文提出了一种新颖的设计，使用人类视觉系统 (HVS) 响应来分解与图像锐度水平密切相关的有意义的特征，旨在解决现有无参考图片清晰度评估技术的高计算成本和缺乏可扩展性的问题，并在数字病理学领域创建了名为 'FocusPath' 的基准数据库，最终提出了一种称为 'HVS-MaxPol' 的创新 NR-ISA 度量，其速度、准确性和可扩展性均表现最佳。