Data Compression Sub-Group Selection for Image Noise Reduction
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Solution Overview
Problem
Conventional video noise reduction methods require significant buffer space and complex hardware, making them unsuitable for systems with limited storage and high power consumption, especially when using encoding standards like MPEG-2 and H.264/AVC for image noise reduction.
Innovation Solution
A data compression method that divides data into sub-groups, applies multiple compression algorithms, selects a preferred algorithm based on error values, and compresses each sub-group to generate compressed data units, which can be decompressed for image noise reduction, thereby reducing buffer space requirements and simplifying hardware implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-image averaging method is used for noise reduction, then noise removal efficiency is improved, but buffer space requirement increases
Solution Approach 1:
The patent divides the image data into multiple sub-data groups (e.g., 4 sub-groups) and processes each sub-group independently through compression and noise reduction. This segmentation allows the system to maintain noise removal effectiveness while reducing the total buffer space required, as each sub-group can be processed with smaller buffer allocations rather than requiring large buffers for the entire image.
2Reliability
If conventional compression algorithms (MPEG-2, H.264) are used, then image quality at low bandwidth is improved, but hardware complexity and power consumption increase
Solution Approach 1:
The patent employs a simplified compression algorithm that is easier to implement in hardware compared to conventional complex algorithms like MPEG-2 or H.264. This simpler algorithm achieves sufficient compression for noise reduction purposes without requiring complex hardware structures, thereby reducing power consumption and hardware complexity while maintaining acceptable image quality.
3Reliability
If more frames are referenced for noise reduction, then noise removal performance is improved, but buffer space requirement increases
Solution Approach 1:
The patent segments the image into sub-data groups and processes them independently, which allows the system to achieve effective noise reduction without requiring multiple reference frames. The segmentation approach enables noise reduction through local processing rather than relying on temporal correlation across multiple frames, thereby reducing buffer space requirements.
Data Source
AI summary
A data compression method, for compressing at least portion of data of a data group, comprising: defining X sub data groups, wherein each of the sub data groups comprises a portion of the data group; compressing each of the sub data groups via Y compression algorithms, to generate Y compression results for each of the sub data groups, wherein X and Y are positive integers and X is at least 2; selecting a preferred compression algorithm for each of the sub data groups according to corresponding ones of the Y compression results; and compressing the sub data group by the preferred compression algorithm thereof to generate a plurality of compressed data units.