This work extends the watermarking method proposed by Kutter et al. to increase the watermark decoding performance for textured or busy images. The proposed algorithm modifies watermark embedding rule to utilize image characteristics, like local standard deviation and gradient magnitude, in order to increase the decoding accuracy for busy images. The method does not need original image for decoding and controls the watermark embedding process at encoder site, resulting in a more accurate decoding.
The last decade witnessed the improvement of strong compression technics over audio-visual data and the development of world wide communications of information. These innovations gave birth to unforeseen requirements like storing these information, indexing and retrieving them for subsequent usages. Standardization of compression of multimedia contents is rapidly accepted as a need for encoding/decoding audio-visual data regardless of machine and environment. However standardization for indexing these materials still remains a puzzle which disables browsing audio-visual data regardless of machine and environment. MPEG-7 standardization group aims to create the standard syntax to access to multimedia content. This paper puts forward a next step, the extraction of user preferences and matching them with MPEG-7 coded media content for quick and smart browsing.
This paper addresses the segmentation of a video sequence into shots, specification of edit effects and subsequent characterization of shots in terms of color and motion content. The proposed scheme uses DC images extracted from MPEG compressed video and performs an unsupervised clustering for the extraction of camera shots. The specification of edit effects, such as fade-in/out and dissolve is based on the analysis of distribution of mean value for the luminance components. This step is followed by the representation of visual content of temporal segments in terms of key frames selected by similarity analysis of mean color histograms. For characterization of the similar temporal segments, motion and color characteristics are classified into different categories using a set of different features derived from motion vectors of triangular meshes and mean histograms of video shots.
The MPEG-4 object-based coding standard, designed as a common platform for all multimedia applications, is inherently well-suited for video indexing applications. To fully exploit the advantages offered by MPEG-4, however, a reconsideration of existing indexing strategies is required. This paper proposes a new object-based framework for video indexing and retrieval that treats as the basic indexing unit the object itself, where changes in content are detected through observations made on the objects in the video sequence. We present a temporal segmentation algorithm that is designed to automatically extract key frames for each video object in an MPEG-4 compressed sequence based on the prediction model chosen by the encoder for individual macroblocks. An extension to the existing MPEG-4 syntax is presented for conducting and facilitating vast database searches. The data presented in the proposed 'indexing field' are: the birth and death frames of individual objects, global motion characteristics/camera operations observed in the scene, representative key frames that capture the major transformations each object undergoes, and the dominant motion characteristics of each object throughout its lifetime. We present the validity of the proposed scheme by results obtained on several MPEG-4 test sequences.
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