METHODS AND SYSTEMS FOR COMPRESSING VIDEO DATA

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METHODS AND SYSTEMS FOR COMPRESSING VIDEO DATA - A simplified explanation of the abstract

This abstract first appeared for US patent application 18296797 titled 'METHODS AND SYSTEMS FOR COMPRESSING VIDEO DATA

The method described in the abstract involves compressing a video stream by extracting Key Point Descriptors (KPDs) and processing frames using Principle Component Analysis (PCA) followed by vector quantization.

Frames of the video stream are retrieved and KPDs are extracted for each sequential frame.
Frames are processed using PCA and vector quantization to create quantized explained variance matrices.
Quantized explained variance matrices and KPDs are stored for each frame.

Potential Applications: - Video streaming services - Video surveillance systems - Video editing software

Problems Solved: - Efficient video compression - Reduced storage requirements for video data

Benefits: - Improved video streaming performance - Lower storage costs for video data

Commercial Applications: Title: "Enhanced Video Compression Technology for Improved Streaming Performance" This technology can be used in video streaming services to enhance the quality of streaming while reducing storage costs. It can also be applied in video surveillance systems to improve data storage efficiency.

Questions about the technology: 1. How does this method compare to traditional video compression techniques? - This method offers improved compression efficiency and reduced storage requirements compared to traditional techniques. 2. What impact does the use of PCA and vector quantization have on the quality of the compressed video stream? - PCA and vector quantization help maintain the quality of the video stream while reducing storage space.

Original Abstract Submitted

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.