Video Processing Apparatus for Content-Aware Frame Segmentation
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Solution Overview
Problem
Conventional indexing techniques are insufficient for handling the increasing diversity of video content types from various broadcasting sources, such as terrestrial TV, satellite, and cable, due to differences in segmentation patterns associated with features like caption size, layout, and timing.
Innovation Solution
A video processing apparatus that specifies start frames for viewing segments based on type-specific specifying information, using a combination of video and audio analysis to accurately segment content into coherent segments, with features like large-caption, small-caption, and transition frames being detected and used to determine presentation and start frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional indexing techniques are used, then processing is simple, but segmentation accuracy deteriorates for diverse content types
Solution Approach 1:
The system changes parameters by storing multiple pieces of specifying information, each with different detection conditions and frame specification rules tailored to specific content types (news programs, documentaries, dramas). The type determination unit identifies content type first, then selects the appropriate specifying information, allowing accurate segmentation for each genre without using a single complex algorithm for all content.
Solution Approach 2:
The indexing system is segmented into distinct functional units: type determination unit, specifying information storage, and frame specification unit. Each unit handles a specific aspect of the indexing process, allowing the system to manage complexity through modular design while achieving high segmentation accuracy for diverse content types.
2Measurement precision
If type-specific specifying information is used, then segmentation accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-storing multiple pieces of specifying information for different content types before actual indexing occurs. The type determination unit quickly identifies the content type, and the corresponding pre-prepared specifying information is immediately applied, avoiding the need to analyze and determine segmentation parameters in real-time, thus reducing processing time while maintaining high accuracy.
3Measurement precision
If multiple detection conditions are applied, then indexing accuracy improves, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically determining content type and selecting the appropriate specifying information without user intervention. The type determination unit and frame specification unit work autonomously to index content based on pre-configured rules for different genres, eliminating the need for users to manually configure detection parameters or understand complex indexing settings, thus maintaining high accuracy while ensuring ease of operation.
Data Source
AI summary
A rule storage unit stores a plurality of pieces of specifying information each showing a feature of frames to be specified as start frames, and each corresponding to a different type of content. A program obtaining unit obtains a content of which start frames are to be specified. An information obtaining unit obtains type information showing the type of the obtained content. A selecting unit obtains, from the rule storage unit, apiece of specifying information corresponding to the obtained type information. A specifying unit specifies, as the start frames, frames in the obtained content having the feature shown by the obtained piece of specifying information.