Implementation and Benchmarking of Perceptual Image Hash Functions

This thesis proposes a novel benchmarking framework, called Rihamark, for perceptual image hash functions. Subsequently, four different perceptual image hash functions were benchmarked: A discrete Cosine transform based, a Marr-Hildreth operator based, a radial variance based and a block mean value based image hash function. pHash, an open source implementation of various perceptual hash functions, was used to benchmark the first three functions. The latter, the block mean value based image hash function was implemented by the author of this thesis himself.