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Video hashing is a popular research topic in the fields of multimedia information and security because its fast matching and low-cost storage characteristics are widely used in many applications (video copy detection, video retrieval, video authentication, etc.). This paper describes a compact video hashing method with a rotation- and translation-invariant model (RTIM). The key contribution of this approach is that it innovatively reconstructs an input video into a 3D RTIM by combining ring partition and a pipeline histogram; this is a first in video hashing and helps make video hashes resistant to rotation and translation. Then, the proposed model is decomposed via Tucker decomposition, and the generated core tensor is used to produce a compact hash. As the core tensor is a compressed version of the original tensor, hash construction with the core tensor makes RTIM hashing compact and achieves desirable discrimination ability. Different from existing video hashing algorithms, RTIM hashing can not only resist many commonly used digital operations, especially video rotation and cyclic frame shifting, but also achieve good discrimination ability. Various experiments demonstrate the effectiveness of our algorithm. Receiver operating characteristic curve comparisons show that compared with the state-of-the-art video hashing algorithms, RTIM hashing is more robust and compact. [ABSTRACT FROM AUTHOR]

Video hashing is a novel technique of multimedia processing and finds applications in video retrieval, video copy detection, anti-piracy search and video authentication. In this paper, we propose a robust video hashing based on discrete cosine transform (DCT) and non-negative matrix decomposition (NMF). The proposed video hashing extracts secure features from a normalized video via random partition and dominant DCT coefficients, and exploits NMF to learn a compact representation from the secure features. Experiments with 2050 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 illustrate that the proposed video hashing outperforms some state-of-the-art algorithms in classification between robustness and discrimination. [ABSTRACT FROM AUTHOR]

We propose a novel perceptual image hashing based on weighted discrete wavelet transform (DWT) statistical features. This hashing converts input image into a normalized image by bi-linear interpolation and color space conversion, extracts edge image of the normalized image via Canny operator, and divides the edge image into non-overlapping blocks. For each block, a three-level 2D DWT is applied to obtain different sub-bands and the weighted sum of the DWT statistics of these sub-bands is calculated. Finally, image hash is generated by concatenating and quantizing these weighted DWT features. Similarity of image hashes is measured by Euclidean distance. The Copydays dataset and the Uncompressed Color Image Database (UCID) are both used to evaluate classification between robustness and discrimination. Receiver operating characteristics curve comparisons illustrate that our hashing is superior to some state-of-the-art algorithms in classification performance with respect to robustness and discrimination. The LIVE Image Quality Assessment Database is used to validate our application in reduced-reference image quality assessment. Experimental results show that our hashing has better performance in image quality assessment than two popular measures, i.e. peak signal-to-noise ratio and structural similarity. [ABSTRACT FROM AUTHOR]