Video Noise Estimation Using Block Classification
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Solution Overview
Problem
Traditional video processing techniques fail to effectively determine noise characteristics in digital video, leading to either over-softening or insufficient noise reduction, and often require excessive computational resources.
Innovation Solution
A video processing system that filters video data into blocks, classifies them as either flat or detailed, and determines noise characteristics such as standard deviation or median absolute deviation, using high-pass directional filters and estimation modules to improve noise reduction techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video processing techniques are used to determine noise characteristics, then the process is simple, but the noise reduction effectiveness is poor leading to over-softening or insufficient noise reduction
Solution Approach 1:
The video data is divided into multiple frames, and each frame is partitioned into blocks that are further classified into flat blocks and detailed blocks. This segmentation allows for separate noise characteristic determination in different regions, improving measurement precision without requiring excessive system complexity.
Solution Approach 2:
The patent applies different processing approaches to different regions of the video data by classifying blocks as flat or detailed. Flat blocks undergo one type of noise analysis while detailed blocks undergo another, allowing locally optimized noise characteristic determination that improves overall accuracy.
2Manufacturing precision
If traditional noise reduction techniques are applied without accurate noise characteristics, then the processing is fast, but the result is either over-softening or under-reducing noise
Solution Approach 1:
The patent performs preliminary classification of video blocks into flat and detailed categories before applying noise reduction. This preliminary action enables subsequent noise reduction steps to be tailored to each block type, ensuring high noise reduction quality while maintaining processing efficiency through pre-organized data structures.
Solution Approach 2:
The patent determines specific noise parameters (standard deviation for flat blocks, median absolute deviation for detailed blocks) and uses these parameters to control the noise reduction process. By changing and adapting parameters based on local video characteristics, the system achieves high noise reduction quality without sacrificing processing speed.
3Measurement precision
If comprehensive noise analysis is performed on all video blocks, then the noise characteristic accuracy is high, but the computational resources required are excessive
Solution Approach 1:
The patent applies different measurement approaches based on local block characteristics: standard deviation is computed for flat blocks while median absolute deviation is used for detailed blocks. This local differentiation maintains measurement accuracy for each block type while reducing overall computational burden by avoiding unnecessary complex calculations in every region.
Solution Approach 2:
The patent performs partial noise analysis by focusing computational effort only on relevant blocks. Flat blocks are analyzed using standard deviation while detailed blocks use median absolute deviation, avoiding excessive computation in regions where it is not needed and maintaining accuracy where it is critical.
Data Source
AI summary
Systems and methods are provided for determining a characteristic of video data. A set of N frames of the video data is obtained and filtered using at least one filter to produce a set of N×T blocks of filtered video data, where T is a partition size associated with the at least one filter. Each block in the set of N×T blocks is classified as either a first type block or a second type block. A subset of blocks in the set of N×T blocks is associated with a corresponding frame from the set of N frames. The characteristic of video data is determined based, at least in part, on the subset of blocks in the set of N×T blocks that are associated with the frame.