KEYWORDS: Camera shutters, Digital cameras, Cameras, Digital imaging, Light sources and illumination, Digital image processing, Image processing, 3D image processing, Image resolution, Electrical engineering
To achieve auto exposure in digital cameras, image brightness is widely used because of its direct relationship with exposure value. To use image entropy as an alternative statistic to image brightness, it is required to establish how image entropy changes as exposure value is varied. This paper presents a mathematical proof along with experimental verification results to show that image entropy reaches a maximum value as exposure value is varied by changing shutter speed or aperture size.
Shannon entropy as a measure of image information is extensively used in image processing applications. This measure
requires estimating a high-dimensional image probability density function which poses a limitation from a practical
standpoint. A number of approaches have been introduced in the literature for estimating image spatial entropy based on
the assumption of Markovianity or homogeneity. This paper provides an overview of these existing approaches and their
differences with Shannon entropy. These definitions are compared by applying them to synthesized test images. These
images are designed in such a way that the spatial arrangements of pixels are changed without altering the histogram,
thus allowing the emphasis to be placed on evaluating image spatial entropy. Furthermore, the computational complexity
aspect of the definitions are discussed. The comparison results show that although the definition of image spatial entropy
based on Aura Matrix provides the most effective outcome among the existing definitions, there are still deficiencies
associated with this definition.