Electronics and Telecommunications Research Institute

Samsung Electronics Co., Ltd.

Chee Sun Won

Sung Min Kim

Hyun Jin Kim

Soo Jun Park

Mi Suk Huh

Dae Youb Kim

Dong See Choi

Seong Joon Yoo

Dong Kwon Park

Hwan Mook Kim

Soo-Jun Park

Myung Gil Jang

Sang-Kyu Park

Myung-Gil Jang

Sung Hee Park

Hwan Joon Kim

Sung-Hee Park

Dong-Kwon Park

Hyunjin Kim

An apparatus and method for video watermarking are provided. The apparatus and method include receiving a bit stream encoded by a video encoder by a spatial domain information analyzer and extracting information from a spatial domain of an image with respect to code words forming the bit stream, grouping the code words for each segmented domain by a domain classifier based on the information from the spatial domain, determining a sign of the each segmented domain by a domain sign determiner and inserting a watermark in the each segmented domain according to the sign determined by the domain sign determiner by a watermark bit inserter.

Method and apparatus for inserting video watermark in compression domain

A video watermarking apparatus and method are provided, including an information provider for providing spatial domain segmentation information of an image with respect to a compressed video, a grouping unit for grouping the compressed video, based on the spatial domain segmentation information, and generating a grouped spatial domain, an extraction domain selector for selecting an extraction domain for extracting a watermark from the grouped spatial domain, and a watermark extractor for extracting a watermark bit inserted in the extraction domain.

Video watermarking apparatus in compression domain and method using the same

A method for constructing a database having digital video data information representing a plurality of video sequence is disclosed. The method includes the steps of: a) partitioning each image frame of each video sequence into L number of sub images; b) generating L number of edge histograms for each image frame; c) normalizing the edge histogram bins to thereby generate M number of normalized edge histogram bins; d) calculating M representative edge histogram bins in order to generate L number of representative edge histograms based on the normalized edge histogram bins; and e) non-linearly quantizing the representative edge histogram bins to generate M number of quantization index values for the each representative edge histogram, to be stored in the database.

Non-linear quantization and similarity matching methods for retrieving video sequence having a set of image frames

A method for generating a block-based image histogram from data compressed by JPEG, MPEG-1, and MPEG-2, or uncompressed image data employing block-based linear quantization to generate histograms that include color, brightness, and edge components. The edge histogram, in particular, includes the global edge features, semi-global edge features, and local edge features. The global edge histogram is based on image blocks of the entire image space. The local edge histogram is based on a group of edge blocks. The semi-global edge histogram is based on the horizontally and the vertically grouped image blocks. A method for generating block-based image histogram with color information and brightness information of image data in accordance with an embodiment of the present invention extracts feature information of an image in terms of the block and updates global histogram bins on the basis of the feature information. The method for generating block-based image histogram with color information and brightness information of image data minimizes quantization error by employing linear weight and updates values of histogram bins. The error that occurs at a boundary between bins of the histograms and the linear weight depends on the distance between the histogram bins.

Method for generating a block-based image histogram

A method for constructing a database having image information representing a plurality of images including the steps of: a) calculating L&#xd7;5 number of normalized edge histogram bins to generate L number of edge histograms of a target image, wherein each edge histogram has five normalized edge histogram bins and represents a spatial distribution of five reference edges in a sub-image and L is a positive integer, wherein the reference edges include four directional edges and a non-directional edge; b) non-linearly quantizing the L&#xd7;5 number of normalized edge histogram bins to generate L&#xd7;5 number of quantization index values for the target image; c) storing the L&#xd7;5 number of quantization index values to the database; and d) repeating the steps a) to c) until all of the stored images are processed to thereby construct the database having the image information.

Non-linear quantization and similarity matching methods for retrieving image data

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Seoul, South Korea

Chee Sun Won