Encoder-Integrated Recursive Bilateral Filter for Video Denoising
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video denoising algorithms are computationally expensive and often result in blurring details or artifacts, especially in texture-rich areas, and require costly motion estimation, limiting their efficiency and effectiveness in real-time video encoding.
Innovation Solution
The integration of a recursive bilateral filter into the video encoder, which uses a similarity term to downweight unreliable input samples and adapt to local signal characteristics, reducing computational overhead and preserving edge details without the need for extensive motion estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If temporal denoising algorithms are used to preserve details, then detail preservation is improved, but computational cost increases due to motion estimation requirements
Solution Approach 1:
The patent combines denoising filtering with motion compensation in a unified processing stage. The filter uses motion-compensated prediction to guide the denoising process, merging what were previously separate operations into a single integrated step that achieves both detail preservation and computational efficiency.
Solution Approach 2:
The patent employs adaptive filtering parameters that dynamically adjust based on local image characteristics and motion information. The filter strength and characteristics are modified according to the predicted motion vectors and local variance, allowing the system to adapt to different regions and preserve details while reducing computation where possible.
2Productivity
If spatial denoising algorithms are used to reduce computation, then computational efficiency is improved, but detail preservation deteriorates due to blurring
Solution Approach 1:
The patent performs motion compensation and prediction before the denoising filtering step. By pre-computing motion vectors and predicted frames, the system prepares the data in advance to guide the filtering process, ensuring that only necessary computational steps are performed while maintaining detail integrity.
Solution Approach 2:
The patent applies different filtering strategies to different regions of the image based on local characteristics such as texture, edge density, and motion magnitude. Regions with high motion or complex textures receive different treatment compared to smooth areas, allowing efficient computation in simple regions while preserving details in complex regions.
3Reliability
If denoising is applied before video encoding to enhance quality, then video quality is improved, but computational intensity increases substantially
Solution Approach 1:
The patent integrates denoising into the encoding pipeline continuously, processing frames as they are generated rather than applying heavy denoising separately. The filter operates continuously during encoding, using the encoded data and motion information to guide the denoising process, thereby maintaining quality while reducing overall computational burden through continuous integration.
Solution Approach 2:
The patent introduces motion-compensated prediction as an intermediary step between the original frame and the denoising filter. This intermediary uses motion vectors to create predicted frames that guide the filtering process, allowing the system to achieve effective denoising with reduced computational intensity by using the prediction as a guide rather than performing exhaustive search.
Data Source
AI summary
Embodiments are provided herein to achieve video or image sequence encoding with an improved denoising algorithm that is both efficient computationally and has acceptable overhead cost in comparison to other denoising schemes for video encoding. The embodiments include using recursive bilateral filtering as part of the denoising algorithm, which is integrated into a video encoder to overcome limitations of other encoder-integrated denoising algorithms. An embodiment method includes receiving, at a filtering and residual computation function at the encoder, a macro block comprising a plurality of pixels. The filtering and residual computation function also receives, from a motion estimation function at the encoder, a reference block. The reference block comprises a plurality of reference pixels corresponding to the macro block. The filtering and residual computation function further applies a recursive bilateral filter function to each of the pixels of the macro block using the reference pixels of the reference block.