In order to adjust the smoothing parameter of the weight function pixel-wise adaptively, the uncertainty of a pixel is utilized as an indicator of image contents.
A proper distribution of the points in the plane, and more precisely in the well-defined region mentioned above, can be taken as an indicator of image quality (see also [26].) However, no particular distribution can be used as a requirement for image quality in general because good-looking images exist with all sorts of distributions; thus, more indicators are needed.
The size of image is in direct proportion to the payload while the embedding intensity is in inversely proportional to the payload because higher bits embedding generates more noise than lower bits embedding does and the noise is the normalized indicator of image distortion.
Resolution, the ability to distinguish two adjacent features, is an indicator of image quality.
However, the hill-climbing search algorithm can only provide the relative indicators of the image sharpness, and the sharpness evaluation is vulnerable to the noise interference, which can make the result appear to the local peaks.
However, more pathological indicators of body image disturbance such as overvaluation of weight and shape, or preoccupation of weight/shape may vary across age groups.
To investigate these possibilities, we used Ca2+ indicators to image the activity of motoneurons, and of interneurons in layers upstream of motoneurons, at different O2 environments.
The absolute value of the image gradient |∇I| is taken as a simple indicator of the image contrast C and used as a metric to calculate the scalar weight map.
Compared with PSNR, SSIM is found to be a better indicator of perceived image quality than mean-squared error which is the theoretical basis of PSNR [28, 29].
Because of the observable correlation between SSI and the quality of the OCT scan, this parameter was used as indicator of the image quality in our study.
