Media Processing Unit for Dynamic Ad Insertion Point Ranking
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Solution Overview
Problem
The challenge in media processing is to automatically and dynamically insert advertisements into media streams without degrading the user experience, as inappropriate placement of ads can severely impact viewer experience.
Innovation Solution
A method that divides a media stream into shots and scenes, generates insertion points based on saliency and distance metrics to maximize the attractiveness and uniform distribution of ads, using a media processing unit comprising a shot detector, scene transition analyzer, and insertion point ranker to select optimal ad insertion points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ads are inserted dynamically into media stream, then advertising effectiveness is improved, but user experience deteriorates due to inappropriate placement
Solution Approach 1:
The system performs preliminary analysis of the media stream to identify scene boundaries, shot transitions, and content characteristics before ad insertion. By pre-processing the media content to understand its structure and semantic meaning, the system can predict suitable insertion points that maintain user experience while enabling dynamic ad placement.
Solution Approach 2:
The system uses feedback mechanisms to evaluate the suitability of potential insertion points by analyzing content importance metrics, scene transition points, and user engagement patterns. This feedback loop allows the system to continuously optimize ad placement decisions, selecting positions that minimize user experience degradation while maximizing advertising effectiveness.
2Quantity of substance
If multiple ads are inserted into media stream, then advertising revenue is improved, but ad distribution uniformity deteriorates
Solution Approach 1:
The system segments the media stream into distinct scenes and shots, then distributes ads across these segments based on their characteristics and importance. By dividing the media content into manageable units and strategically placing ads at scene boundaries or transition points, the system achieves uniform distribution of multiple ads throughout the stream without clustering them in specific regions.
3Object-affected harmful factors
If ad insertion points are selected based on content importance, then user experience is improved, but insertion point detection complexity increases
Solution Approach 1:
The system applies local quality analysis by evaluating content importance metrics at specific locations (scene boundaries, shot transitions) rather than analyzing the entire media stream uniformly. By focusing computational resources on key transition points and using localized content analysis, the system maintains high user experience quality while reducing overall detection complexity.
Data Source
AI summary
In accordance with an embodiment of the present invention, a method for inserting secondary content into a media stream includes dividing the media stream having a plurality of frames into a plurality of shots at a processor. The method further includes grouping consecutive shots from the plurality of shots into a plurality of scenes. A first list of insertion points is generated for introducing the secondary content. The insertion points of the first list are boundaries between consecutive scenes in the plurality of scenes. An average insertion point saliency of the media stream is generated at the insertion points in the first list. A second list of insertion points is then generated. The insertion points in the second list are arranged to maximize a function of the average insertion point saliency and a distance between each insertion point in the second list with other insertion points in the second list.