Video Encoder Blurriness Estimation for Bandwidth Reduction
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Solution Overview
Problem
Existing video encoding technologies face challenges in optimizing video encoding data rate while maintaining acceptable visual quality, particularly during the refocusing process in continuous auto-focus systems, which often results in increased computational complexity and bandwidth consumption due to blurriness in video frames.
Innovation Solution
A method where a video capture device estimates the blurriness level of frames during refocusing and communicates this information to the video encoder, allowing the encoder to adjust the quantization parameter and simplify encoding algorithms, such as using integer pixel precision and larger block partitions, to reduce bandwidth usage and computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If video encoding is performed with high computational complexity to improve video encoding data rate, then video encoding data rate is improved, but device complexity increases
Solution Approach 1:
The video capture device performs preliminary blurriness estimation before video encoding, and communicates this information to the video encoder in advance. This allows the encoder to pre-adjust encoding parameters (such as quantization parameter and block partition size) based on the blurriness level, avoiding the need for complex real-time analysis during encoding and thereby reducing overall device complexity while maintaining efficient data rate control
Solution Approach 2:
The patent applies different encoding strategies to different regions or frames based on local blurriness characteristics. By estimating blurriness at the frame level and applying localized encoding adjustments (such as using larger block partitions for blurry regions), the system achieves efficient data rate control without uniformly increasing complexity across the entire encoding pipeline
2Stability of the object's composition
If standard video encoding is applied to frames during refocusing, then encoding consistency is maintained, but bandwidth consumption increases due to unnecessary high data rate allocation for blurry frames
Solution Approach 1:
The patent introduces dynamic adjustment of encoding parameters based on the blurriness level of each frame. The quantization parameter and block partition size are adaptively modified according to the estimated blurriness, allowing the encoding process to respond to changing frame conditions. This dynamic approach reduces bandwidth consumption for blurry frames while maintaining appropriate quality for clear frames, achieving overall bandwidth efficiency without sacrificing encoding consistency
Solution Approach 2:
The system changes key encoding parameters (quantization parameter, block partition size) based on the blurriness estimation. For frames with high blurriness, the quantization parameter is increased and block partition size is enlarged, which reduces the data rate allocated to these frames. This parameter adaptation allows the system to reduce bandwidth consumption for blurry frames while maintaining encoding consistency through systematic parameter control
Data Source
AI summary
This disclosure describes techniques for improving functionalities of a video encoder, using parameters detected and estimated by a front-end video capture device. The techniques may involve estimating a blurriness level associated with frames captured during a refocusing process. Based on the estimated blurriness level, the quantization parameter (QP) used to encode blurry frames is adjusted either in the video capture device or in the video encoder. The video encoder uses the adjusted QP to encode the blurry frames. The video encoder also uses the blurriness level estimate to adjust encoding algorithms by simplifying motion estimation and compensation in the blurry frames.