The use of moments to characterize image functions dates back to Hu [37], who first introduced the 2D geometric moment invariants for pattern recognition.
Therefore, the parameter that can be determined for an image in advance (before filtering) can serve for characterizing image complexity and noise masking effects as well as predicting efficiency of filtering.
We characterize image filtering efficiency not only by the conventional criteria, such as peak signal-to-noise ratio (PSNR) (or mean square error (MSE)), but also using metrics that describe image visual quality [23].
Thus, also from example one can see that PSNR-HVS-M characterizes image visual quality more adequately than conventional PSNR. Figure 2 The test image #16 corrupted by i.i.d.i.d
Although figure of image quality and information capacity indexes were tested on a generic software package, their utilization in characterizing image quality in dedicated medical image software will be useful.
Although the implementation of the figure of image quality and information capacity concepts was done with a commercial generic processing tool, their utilization in characterizing image quality in dedicated medical image software may be of importance.
Although generally a smaller residual wavefront error implies better AO performance, in some situations when the wavefront errors are localized, wavefront rms would become inaccurate in characterizing image quality.
It is often difficult to come up with a well-principled approach to the selection of low-level features for characterizing images for content-based retrieval.
All the retrieval experiments substantiate the ability of the proposed HDLA model in discovering latent topics by grouping semantically similar visual words to characterize images at a much higher-level of abstraction.
This FIQ characterizes images in terms of resolution and noise.
