CNN prediction performance with <i>N</i>_<i>i</i> or <i>P</i>_<i>n</i> changes.

<p>CNN prediction performance with <i>N</i>_<i>i</i> or <i>P</i>_<i>n</i> changes.</p

Performance evaluation with PLCC on Gaussian blurring images.

<p>Performance evaluation with PLCC on Gaussian blurring images.</p

The proposed BISA system.

<p>A gray-scale image is pre-processed with local contrast normalization and then a number of image patches are randomly cropped for CNN training, validation and final testing.</p

One trained kernel visualized by using “monarch.bmp”.

<p>After convolutional filtering with the trained kernel, edge structures is hard to notice in heavily blurred images (<i>y</i>₁₁), while fine structures can be seen in relatively high-quality images (<i>y</i>₉₆).</p

CNN performance with regard to kernel number and kernel size.

<p>CNN performance with regard to kernel number and kernel size.</p

The time spent on score prediction of image sharpness.

<p>Several algorithms show promise in real-time image sharpness estimation.</p

Performance evaluation of SROCC on Gaussian blurring images.

<p>Performance evaluation of SROCC on Gaussian blurring images.</p

Example of Gaussian blurring images in four databases.

<p>Example of Gaussian blurring images in four databases.</p