Video Frame Compression with KPDs and PCA for Lower Bandwidth
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Solution Overview
Problem
Existing video recording systems face challenges in balancing storage space and communication bandwidth requirements with maintaining sufficient video quality, as recording at high quality consumes substantial resources while reduced quality may not meet user needs.
Innovation Solution
The method involves extracting Key Point Descriptors (KPDs) and applying Principle Component Analysis (PCA) followed by vector quantization to compress video frames, using quantized explained variance matrices to represent frames with reduced dimensions, and storing KPDs for decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If video streams are recorded with high video quality settings, then video quality is maintained, but storage space and communication bandwidth are substantially consumed
Solution Approach 1:
The patent extracts and stores only the most important visual information from video frames using Key Point Descriptors (KPDs) that identify salient objects and features. By separating essential visual content from redundant data, the system maintains video quality for important elements while significantly reducing overall storage requirements.
Solution Approach 2:
The patent applies different compression strategies to different regions of video frames. High-quality representation is maintained for regions containing Key Points and salient objects, while other regions use standard compression. This localized quality approach ensures important visual information is preserved while reducing overall data volume.
2Manufacturing precision
If video streams are recorded with high video quality settings, then video quality is maintained, but communication bandwidth is substantially consumed
Solution Approach 1:
The system extracts only the most critical visual information using KPDs and stores it in a compact format. This extracted representation can be transmitted over communication networks with much lower bandwidth requirements while still preserving essential video quality for salient content.
Solution Approach 2:
The patent transforms video data from traditional pixel-based representation to a parameter-based representation using KPDs and Principal Component Analysis (PCA). This parameter transformation reduces the dimensionality and data volume, enabling efficient transmission while maintaining quality for important visual elements.
3Quantity of substance
If video quality is reduced to save storage space, then storage requirements are decreased, but video quality may not meet user needs
Solution Approach 1:
Instead of uniformly reducing video quality, the system extracts and prioritizes the most important visual information using KPDs. This selective extraction ensures that essential visual content is preserved in the compressed representation, meeting user quality needs while achieving storage reduction.
Solution Approach 2:
The patent maintains high quality representation for specific regions containing Key Points and salient objects, while applying more aggressive compression to other regions. This ensures that video quality meets user needs for important content while achieving overall storage reduction.
4Quantity of substance
If video quality is reduced to save bandwidth, then bandwidth requirements are decreased, but video quality may not meet user needs
Solution Approach 1:
The system extracts only the most essential visual information using KPDs for transmission. This extracted representation maintains quality for salient content while significantly reducing bandwidth requirements, ensuring user needs are met with efficient data transmission.
Solution Approach 2:
The patent transforms video data into a compact parameter representation using KPDs and PCA, reducing the data volume for transmission while preserving essential visual quality. This parameter-based approach enables efficient bandwidth utilization without sacrificing user-perceived quality for important content.
Data Source
AI summary
A method for compressing a video stream includes retrieving a plurality of frames corresponding to the video stream. For each of two or more sequential frames of the plurality of frames of the video stream, the method includes extracting Key Point Descriptors (KPDs) for the respective frame and processing the respective frame using Principle Component Analysis (PCA) followed by vector quantization, resulting in a quantized explained variance matrix for the respective frame. The quantized explained variance matrix for the respective frame is stored. The KPDs for the respective frame are stored.