Video Coding Noise Reduction via Minimum Quantization Parameter
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Solution Overview
Problem
Existing video coding technologies face inefficiencies due to input noise, which reduces coding accuracy and bandwidth utilization, especially in low noise levels and highly textured images, and often require resource-intensive processing.
Innovation Solution
The method involves determining an estimated noise level for video frames by convolving pixel values and averaging convolved values within a defined range, using a constant multiplier and correction coefficient, to filter noise and set a minimum quantization parameter for encoding, thereby optimizing noise reduction and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video coding is used without noise estimation, then coding process is simple and fast, but coding accuracy deteriorates due to input noise reducing bandwidth utilization
Solution Approach 1:
The patent performs noise level estimation before the actual video coding process. By determining the noise level of the input video frame in advance and using it to set the minimum quantization parameter, the coding process can proceed with optimized parameters that account for noise characteristics, thereby improving coding accuracy without adding significant complexity during the main encoding phase.
2Measurement precision
If resource-intensive noise reduction processing is applied, then noise reduction performance improves, but resource utilization deteriorates
Solution Approach 1:
The patent changes the parameter approach by using a constant multiplier (e.g., 1/6 * sqrt(pi/2)) and correction coefficient instead of complex adaptive noise reduction algorithms. This parameter-based method estimates noise level efficiently by convolving pixel values and averaging results, achieving good noise reduction accuracy while consuming minimal processing resources compared to traditional intensive noise reduction techniques.
3Measurement precision
If quantization parameter is reduced to improve coding quality, then coding quality improves, but bandwidth utilization deteriorates due to increased data transmission
Solution Approach 1:
The patent implements a feedback mechanism where the estimated noise level is used to dynamically determine the minimum quantization parameter. This feedback loop allows the system to adapt the quantization parameter based on actual noise conditions in the video frame, ensuring optimal balance between coding quality and bandwidth utilization by avoiding excessive quantization in low-noise regions while maintaining adequate quality in high-noise regions.
Data Source
AI summary
Efficient noise reduction coding may include generating, by a processor, an encoded frame by encoding an input video frame. Encoding the input frame includes determining an estimated noise level for the input video frame, determining a minimum quantization parameter level based on the estimated noise level for the input video frame, determining a quantization parameter value for encoding the input frame such that the quantization parameter value is at least the minimum quantization parameter level, obtaining the encoded frame by encoding the input frame in accordance with the quantization parameter level, including the encoded frame in an output bitstream, and outputting the output bitstream.