1. A method for determining an estimated clutter level of an input digital image, the method being performed at least in part using a data processor, comprising: designating a set of reference features; partitioning the input digital image into small sub-images; determining a set of image features by automatically analyzing the sub-images; associating each of the determined image features with one of the reference features; forming a frequency distribution representing a frequency of occurrence of the associated reference features for the input digital image; analyzing the frequency distribution to determine an inequality index, the inequality index being a measure of statistical variation of the reference features; comparing the inequality index to a predefined threshold to classify the input digital image as a rich-content image or a low-content image; if the input digital image is classified as a rich-content image: analyzing the input digital image to automatically determine scene content features relating to spatial structures in the input digital image or semantic content of the input digital image; and determining the estimated clutter level responsive to the scene content features; and if the input digital image is classified as a low-content image: analyzing the input digital image to automatically determine an overall luminance level; and determining the estimated clutter level responsive to the determined overall luminance level.