Wavelet Pyramid Video Data Representation for Fast Retrieval
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Solution Overview
Problem
Current video data retrieval systems face challenges with large volumes of data, as they are not designed for efficient content-based indexing and retrieval, leading to slow and complicated processes, especially with formats like MPEG-4, and often require manual annotation and keyword-based indexing.
Innovation Solution
A novel video data representation using a wavelet transform process to segment and compress video data, emphasizing object detection and de-emphasizing background information, coupled with a content-based retrieval framework that enables fast and effective video structure analysis and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video data is stored in compressed formats like MPEG-4, then storage efficiency is improved, but retrieval speed and ease of content-based indexing deteriorate
Solution Approach 1:
The video data is segmented into wavelet pyramid representations at multiple decomposition levels, where each level captures different frequency components. This segmentation allows the retrieval system to operate on specific frequency bands rather than processing entire video frames, significantly reducing computational complexity and improving retrieval speed while maintaining storage efficiency.
Solution Approach 2:
The patent transforms video data from the traditional time-spatial domain into a multi-dimensional wavelet frequency domain. By representing video frames as wavelet pyramids with multiple decomposition levels and frequency sub-bands, the system enables content-based indexing and retrieval operations to be performed on specific frequency components, avoiding the need to process complete compressed video streams and thus improving retrieval efficiency.
2Ease of manufacture
If manual keyword annotation is used for video indexing, then implementation simplicity is improved, but automation level and scalability deteriorate
Solution Approach 1:
The system performs self-service by automatically extracting meaningful content from video data through wavelet transform and generating corresponding index structures without requiring manual annotation. The wavelet pyramid representation inherently captures visual features at multiple scales, enabling the system to autonomously create searchable indexes that improve both automation level and scalability while maintaining ease of implementation through standardized processing pipelines.
3Measurement precision
If complete video data is processed for retrieval operations, then retrieval accuracy is improved, but processing time and computational complexity deteriorate
Solution Approach 1:
The patent extracts only the essential frequency components from complete video data by performing retrieval operations on specific wavelet pyramid levels and sub-bands rather than processing entire video frames. This extraction approach maintains retrieval accuracy by focusing on discriminative frequency features while significantly reducing processing time and computational complexity by avoiding unnecessary processing of redundant video data.
Data Source
AI summary
An efficient object-detection-driven video data representation along with a unified content based video compression and retrieval framework and wavelet based searching engine is described to encode the disclosed video data representation. The video data representation and unified compression and retrieval and searching engine together facilitate rapid retrieval of desired video information from large stores of video data.