Video Frame Compression Using PCA and Key Point Descriptors
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Solution Overview
Problem
Existing video recording systems face challenges in managing storage space and communication bandwidth requirements while maintaining sufficient video quality, as recording at high quality consumes substantial resources and 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 techniques like mean subtraction and block-wise PCA to reduce frame dimensions, and storing quantized matrices for compression, with inverse PCA for decompression.
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 significant 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 dramatically reducing storage requirements for the overall video stream.
Solution Approach 2:
The patent applies different processing quality levels to different regions of the video frame. High-quality processing is applied to regions containing Key Point Descriptors (salient objects and features), while other regions use standard compression. This localized quality approach ensures important visual information is preserved while reducing overall storage consumption.
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 essential visual information represented by Key Point Descriptors for transmission and storage. By identifying and transmitting only the most important visual features rather than complete high-resolution frames, the patent significantly reduces communication bandwidth requirements while preserving video quality for salient content.
Solution Approach 2:
The patent transmits high-quality data only for regions containing Key Point Descriptors while using lower-quality compression for other areas. This selective quality transmission optimizes bandwidth usage by allocating communication resources to the most visually important parts of the video stream.
3Quantity of substance
If video streams are recorded with reduced video quality settings, then storage space and communication bandwidth are saved, but video quality may not be sufficient to meet user needs
Solution Approach 1:
The patent extracts Key Point Descriptors that capture the essential visual content of video frames. By storing these extracted features at high quality alongside compressed video data, the system ensures that even when overall video quality is reduced for storage efficiency, the most important visual information remains available in high quality.
4Quantity of substance
If Key Point Descriptors and PCA processing are applied to each frame, then video data size is reduced, but processing complexity increases
Solution Approach 1:
The patent performs Key Point Descriptor extraction and PCA processing as preliminary actions during the video encoding phase. By completing these computationally intensive operations before storage or transmission, the system reduces the data size that needs to be stored or transmitted, while the processing complexity is concentrated in the initial encoding stage rather than during playback or retrieval.
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.