Bitstream Editing for Dynamic Advertisement Insertion
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Solution Overview
Problem
Conventional advertisement broadcast methods often result in irrelevance between program content and advertisements, leading to ineffective advertising due to outdated product placements, as advertisements are not dynamically aligned with the current content being broadcasted.
Innovation Solution
A method and system for bitstream editing that dynamically selects and inserts relevant advertisement bitstreams into a multimedia bitstream by performing keypoint extraction and comparison, allowing for real-time matching of advertisement content with the program content, ensuring high relevance and freshness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional advertisement broadcast methods are used with fixed timeslots, then advertisement scheduling is simple and predictable, but advertisement relevance to program content deteriorates and advertising effectiveness decreases
Solution Approach 1:
The patent implements dynamic advertisement selection by extracting keypoint features from both program content and advertisement content, then matching them in real-time based on similarity calculations. This replaces the static fixed-timeslot approach with a dynamic system that adapts advertisement selection to the actual program content being broadcast, thereby improving advertisement effectiveness while managing complexity through automated feature matching
Solution Approach 2:
The patent replaces the manual or rule-based mechanical advertisement scheduling system with an automated image recognition and pattern matching system. By using keypoint extraction, feature comparison, and similarity calculation algorithms, the system automatically selects and inserts relevant advertisements without manual intervention, resolving the contradiction between effectiveness improvement and complexity increase
2Reliability
If embedded marketing with outdated products is used in programs, then advertisement placement is seamless and relevant to content, but product freshness deteriorates and advertising value decreases
Solution Approach 1:
The patent performs preliminary extraction of keypoint features from advertisement content and stores them in advance. When program content is being broadcast, the system quickly matches pre-extracted advertisement features against current program features, enabling rapid selection of fresh, relevant advertisements without manual planning or outdated product placements
Solution Approach 2:
The system continuously monitors program content through real-time keypoint extraction and uses feedback from similarity calculation results to dynamically adjust advertisement selection. This closed-loop feedback mechanism ensures that only advertisements with high relevance scores (indicating freshness and appropriateness) are selected and inserted, maintaining both relevance and product freshness
3Measurement precision
If manual advertisement selection is used, then system complexity is low and ease of operation is high, but advertisement matching precision with program content deteriorates
Solution Approach 1:
The patent creates feature representations (copies) of both program content and advertisement content through keypoint extraction. Instead of manually comparing entire video streams, the system works with compressed feature vectors that capture essential visual characteristics. This copying approach enables precise automated matching while keeping computational complexity manageable through dimensionality reduction
Solution Approach 2:
The patent transforms complex video content into simplified numerical parameters through keypoint extraction and feature vector representation. By changing the parameter space from raw video pixels to extracted features (such as SIFT, SURF, or ORB descriptors), the system achieves high matching precision through automated comparison of these transformed parameters, resolving the contradiction between precision improvement and system complexity
Data Source
AI summary
A method for bitstream editing is provided. The method includes steps of: fetching a first original image from a source multimedia bitstream; performing variation processing on the first original image to generate a plurality of transformed pictures; performing keypoint extraction according to comparison results of the transformed pictures to obtain a plurality of candidate pixels; locating a first advertisement bitstream from an advertisement bitstream database according to the plurality of candidate pixels; and inserting the first advertisement bitstream to the source multimedia bitstream.