Video Buffer Noise-Adaptive Quantization Bandwidth Reduction
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Solution Overview
Problem
Existing buffer systems for reducing temporal noise in video frames require further compression to reduce bandwidth usage while minimizing visible side effects.
Innovation Solution
A method involving determining the amount of noise in new pixel values, quantizing based on this noise level, and applying weighted compression to update the buffer, reducing bandwidth requirements without degrading image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If compression is applied to reduce bandwidth for buffer operations, then bandwidth requirements are reduced, but visible side effects such as image quality degradation occur
Solution Approach 1:
The patent applies adaptive quantization where the quantization parameter is dynamically adjusted based on the noise level of the current video frame. When noise levels are high, stronger quantization is applied to achieve greater compression. This parameter change allows the system to optimize the balance between bandwidth reduction and image quality preservation according to actual processing conditions.
Solution Approach 2:
The system dynamically adapts its compression strength in response to changing noise conditions. The quantization parameter is not fixed but varies based on real-time noise assessment, enabling the system to optimize bandwidth usage while maintaining acceptable image quality under different video conditions.
2Loss of energy
If higher quantization is applied to compress pixel values, then bandwidth is reduced, but visible side effects increase
Solution Approach 1:
The quantization parameter is changed adaptively based on noise level measurements. The system assesses the noise characteristics of each frame and adjusts the quantization strength accordingly, applying stronger quantization only when necessary to achieve the desired compression without excessive visible artifacts.
Solution Approach 2:
The system incorporates feedback from noise level measurements to control the quantization process. By continuously monitoring the noise characteristics and using this information to adjust quantization parameters, the system achieves a feedback-driven optimization of compression versus quality trade-off.
3Loss of time
If the buffer is updated with compressed data, then bandwidth and latency are reduced, but compression must be applied to stored pixel values
Solution Approach 1:
The compression and quantization operations are performed in advance before the buffer update occurs. By preparing the compressed data in advance based on noise assessment, the system reduces the computational burden during the actual buffer update operation, thereby reducing latency while maintaining necessary compression.
Data Source
AI summary
An image processing system stores a set of pixel values for a set of pixels relating to a sequence of video frames obtained from a buffer. The pixel values have been filtered using a filtering algorithm to reduce temporal noise. A measure noise related to the pixel values is obtained and a current set of pixel values is obtained from a sensor. A new set of pixel values is then determined based on the stored pixel values and the current pixel values using the filtering algorithm to reduce temporal noise. A measure of noise in the new set of pixel values is then determined and the new set of pixel values is quantized. The higher amount of noise in the new set of pixel values, the higher quantizing is performed. The quantized set of pixel values are compressed and the buffer is updated with the compressed quantized pixel values.