Weighted Uniform Distribution Random Number Generation
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Solution Overview
Problem
Conventional random number generation methods for video and audio applications are inefficient due to the need for frequent re-sampling and conditional loops, which hinder pipelining and increase computational intensity.
Innovation Solution
A method for generating random numbers using a sampling distribution composed of a weighted combination of uniform distributions, allowing for the selection of a sample value without conditionals or loops, facilitating efficient pipelining and parallel execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional sampling techniques with rejection are used, then random numbers can be generated, but computational intensity increases and pipelining becomes difficult due to conditional loops
Solution Approach 1:
The patent extracts and eliminates the rejection sampling step from the random number generation process. By using a weighted combination of uniform distributions that directly covers the target distribution range, samples that would previously be rejected are now generated directly, removing the need for conditional loops and re-sampling operations
Solution Approach 2:
The patent performs preliminary action by pre-defining weighted combinations of uniform distributions that map directly to the desired sampling distribution. This pre-computation allows direct sample generation without requiring subsequent rejection checks, as the weighting scheme is designed in advance to produce valid samples
2Reliability
If rejection sampling is used, then random numbers can be generated, but processing time increases due to re-sampling requirements
Solution Approach 1:
The rejection step is extracted and removed from the sampling process. The weighted combination approach generates valid samples directly without requiring rejection and re-sampling, eliminating the time loss associated with iterative correction
Solution Approach 2:
The patent achieves continuous useful action by ensuring every generated sample is valid and usable. The weighted uniform distribution combination is designed so that all generated samples fall within the acceptable range, eliminating idle re-sampling cycles and maintaining continuous productive processing
3Reliability
If conditional loops are used for re-sampling, then correct random numbers can be generated, but pipelining efficiency decreases
Solution Approach 1:
The conditional loop structure is extracted and replaced with direct computation. By using weighted uniform distributions that guarantee valid samples, the patent eliminates the need for conditional branching and iterative loops, enabling straightforward pipelining implementation
Solution Approach 2:
The patent segments the sampling process into fixed-weight combinations of uniform distributions, where each segment corresponds to a specific weight and range. This segmentation allows independent computation of each component without requiring coordination or synchronization through conditional loops
Data Source
AI summary
Systems and methods facilitating random number generation of hashes for video and/or audio are provided. In one embodiment, a system can include: a memory, and a microprocessor that executes computer executable components. The components can include a weighted distribution generation component that can generate a sampling distribution of a weighted combination of uniform distributions, and obtain a sample value from the sampling distribution. In one embodiment, horizontal regions of substantially equal area can be identified. The sample value can be obtained by selecting one of the horizontal regions, and uniformly selecting a coordinate from the horizontal region. The coordinate can correspond to a value on a horizontal axis of the sampling distribution, and the value can be equal to a sample value. The sample value can be employed to compute a hash employed in video and/or audio fingerprinting and/or in computing image descriptors for video.