Perceptual hashing is conventionally used for content identification and authentication. In this work, we explore a new application of image hashing techniques. By comparing the hash values of original images and their compressed versions, we are able to estimate the distortion level. A particular image hash algorithm is proposed for this application. The distortion level is measured by the signal to noise ratio (SNR). It is estimated from the bit error rate (BER) of hash values. The estimation performance is evaluated by experiments. The JPEG, JPEG2000 compression, and additive white Gaussian noise are considered. We show that a theoretical model does not work well in practice. In order to improve estimation accuracy, we introduce a correction term in the theoretical model. We find that the correction term is highly correlated to the BER and the uncorrected SNR. Therefore it can be predicted using a linear model. A new estimation procedure is defined accordingly. New experiment results are much improved.
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