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Scene statistics is a discipline within the field of perception. It is concerned with the statistical regularities related to scenes. It is based on the premise that a perceptual system is designed to interpret scenes.
Biological perceptual systems have evolved in response to physical properties of natural environments. Therefore natural scenes receive a great deal of attention.
Natural scene statistics are useful for defining the behavior of an ideal observer in a natural task, typically by incorporating signal detection theory, information theory, or estimation theory.
Within-domain versus across-domain
Geisler (2008) distinguishes between four kinds of domains: (1) Physical environments, (2) Images/Scenes, (3) Neural responses, and (4) Behavior.
Within the domain of images/scenes, one can study the characteristics of information related to redundancy and efficient coding.
Across-domain statistics determine how an autonomous system should make inferences about its environment, process information, and control its behavior. To study these statistics, it is necessary to sample or register information in multiple domains simultaneously.
The image above was generated from a database of segmented leaves that simultaneously registers natural images (scene information) with the exact locations of leaf boundaries (information about the physical environment). Such a database can be used to study across-domain statistics.
Many papers have been published in the area of scene statistics.