High Accuracy Perceptual Video Hashing via Low-Rank Decomposition and DWT

In this work, we propose a novel robust video hashing algorithm with High Accuracy. The proposed algorithm generates a fix-up hash via low-rank and sparse decomposition and discrete wavelet transform (DWT). Specifically, input video is converted to randomly normalized video with logistic map, and then content-based feature matrices extract from a randomly normalized video with low-rank and sparse decomposition. Finally, data compression with 2D-DWT of LL sub-band is applied to feature matrices and statistic properties of DWT coefficients are quantized to derive a compact video hash. Experiments with 4760 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons indicate that the proposed video hashing more desirable performance than some algorithms in classification between robustness and discrimination.