Video Stream Static Content Detection Using Hash Functions
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Solution Overview
Problem
Existing wireless video transmission technologies face challenges in reducing power consumption and bit rate due to the need for large memory spaces and high costs associated with frame buffer memory for detecting static content in video streams, which is essential for efficient redundancy removal.
Innovation Solution
The implementation of mathematical operations such as hash functions or CRC on video content for static content detection, reducing the amount of data written and read to memory, allowing for on-chip integration of cost-effective memory and power savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frame buffer memory is used to store video data for static content detection, then detection accuracy is improved, but memory cost and power consumption increase
Solution Approach 1:
The patent extracts only the essential features needed for static content detection (hash values or CRC codes) rather than storing and processing entire video frames. This extraction approach maintains detection accuracy while dramatically reducing memory requirements and power consumption associated with storing full frame buffers.
Solution Approach 2:
The patent transforms the video data representation from full pixel data to condensed hash values or CRC codes. This parameter change reduces the data size from megabytes per frame to bytes per frame, enabling static content detection with minimal memory usage and power consumption.
2Measurement precision
If frame buffer memory is used to store video data for static content detection, then detection accuracy is improved, but memory cost increases
Solution Approach 1:
The patent extracts only the essential features needed for static content detection (hash values or CRC codes) rather than storing and processing entire video frames. This extraction approach maintains detection accuracy while dramatically reducing memory requirements and power consumption associated with storing full frame buffers.
Solution Approach 2:
The patent transforms the video data representation from full pixel data to condensed hash values or CRC codes. This parameter change reduces the data size from megabytes per frame to bytes per frame, enabling static content redundancy removal with minimal memory usage and cost.
3Quantity of substance
If mathematical operations like hash functions are used for static content detection, then memory requirements are reduced, but computational complexity increases
Solution Approach 1:
The patent introduces hash functions or CRC codes as intermediary computations that transform video frame data into compact representations. These mathematical operations serve as mediators that reduce data volume while maintaining the ability to detect static content, balancing memory reduction with acceptable computational complexity.
Data Source
AI summary
Methods for removing redundancies in a video stream based on efficient pre-transmission detection of static portions of the video stream. In one embodiment supporting wireless transmission of a video stream having a series of video frames, a mathematical operation (such as a hash function, summing operation or CRC) is performed on (1) a (reconstructed) data block(s) of a video frame in order to generate a first check value and (2) a co-located data block(s) of a second, sequential video frame in order to generate a second check value. The first and second check values are compared to detect static video content in the video stream. When static video content in a video stream is detected, the static nature of the content is indicated in the compressed bit stream and the amount of wirelessly transmitted data corresponding to the static portions of the video stream may be reduced.