Distortion in digital images is very common. To some extent

distorted image can affect research

such as analyzing and understanding images. In addition

the method of calculating the sharpness of an image is essential for the implementation of autofocus. Exploring its deeper mechanisms is of research importance. The performance of no-reference image quality assessment (IQA) has room for improvement. To upgrade the technology of sharpness assessment

an algorithm

which is called GI-F and is based on gradient information and human vision system (HVS) filter

is proposed. HVS is highly sensitive to gradient information. In the proposed algorithm

gradient information is first calculated using a gradient operator that researchers apply when computing image quality. Human studies in neurology also contributed to the development of other disciplines. Among visual cortex neurons

a mechanism occurs in which local excitation with higher amplitude can inhibit other impulses from global region. Therefore

HVS filter based on this characteristic is employed as a weighing function

in which the variable ranging from 0 to 1 stands for the relative impulse produced by each pixel in the picture

to obtain the sum of gradient information which represents the sharpness of image. Performance test can quantitatively evaluate different algorithms from the same perspectives

which generally include root-mean-squared error (RMSE)

Pearson linear correlation coefficient (PCC)

Spearman's rank-order correlation coefficient (SROCC)

and time cost. To supplement

higher PCC and SROCC means that the score calculated using the proposed algorithm is more relevant with human vision system. Meanwhile

lower RMSE shows a smaller difference between two groups of samples. To ensure fairness of comparison among different algorithms

the test should be conducted under the same circumstance. The test is performed on public databases such as LIVE

TID2008

and CSIQ. Calculated results reveal that GI-F outperforms S3

CPBD and LPC-SI by using the metrics of RMSE

PCC and SROCC

which improved by 20.66%

4.61% and 3.33%

respectively. In addition

the proposed method has lower computational complexity

and saves 79.72% computational time

compared with the currently best algorithm

BRISQUE. The proposed algorithm

applying gradient information and HVS filter

costs less time to compute objective sharpness

which is closer to the perceptive sharpness provided in public databases. In addition

such an algorithm can be widely used in sharpness assessment when reference images cannot be provided. In autofocus camera applications

more accurate and more stable sharpness results in improved performance of GI-F compared with other methods. Meanwhile

parallel computation is considered to save additional time as well.