Multi-Mode Video Filtering With Simplified Neural Blocks
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Solution Overview
Problem
Neural network-based filtering techniques for video data are highly complex and require significant processing power, limiting their widespread application in video coding devices.
Innovation Solution
Implement simplified neural network processing blocks, including 1×1 convolutional filters, parametric rectified linear unit (PReLU) filters, and 3×3 convolutional filters, to enhance video data filtering while reducing processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural network-based filtering techniques are applied to video data, then filtering effectiveness is improved, but device complexity and processing power requirements increase
Solution Approach 1:
The neural network filter is divided into multiple processing blocks (first neural network processing block, second neural network processing block, etc.), each handling specific filtering operations. This segmentation allows the complex filtering task to be broken down into manageable stages that can be processed sequentially or in parallel, reducing the computational burden on any single device while maintaining overall filtering effectiveness.
Solution Approach 2:
The patent applies filtering operations selectively to specific regions or blocks of video data rather than processing the entire video stream uniformly. By applying neural network filtering only where needed (e.g., in regions with significant distortion or artifacts), the system achieves effective filtering while reducing overall processing complexity and power consumption.
2Reliability
If neural network-based filtering techniques are applied to video data, then filtering effectiveness is improved, but processing power requirements increase
Solution Approach 1:
The filtering process is segmented into multiple processing blocks that can be executed sequentially or in parallel. This allows the system to distribute the computational workload over time and across different processing units, reducing the peak power requirements while maintaining filtering effectiveness through cumulative processing.
Solution Approach 2:
The system applies neural network filtering selectively to specific regions or blocks of video data rather than processing the entire video stream uniformly. By applying filtering only where needed (e.g., in regions with significant distortion or artifacts), the system achieves effective filtering while reducing overall processing power consumption.
Data Source
AI summary
An example device for filtering video data includes a memory configured to store video data; and a processing system comprising one or more processors implemented in circuitry, the processing system being configured to: apply one or more neural network processing blocks to intermediate filtered video data, each of the neural network processing blocks including a first 1×1 convolutional filter, a parametric rectified linear unit (PReLU) filter, a second 1×1 convolutional filter, and a 3×3 convolutional filter; apply additional neural network processing blocks to output of the one or more neural network processing blocks to form filtered video data; and output the filtered video data.