Adaptive Video Pre-Processing Filtering for Coding Efficiency
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Solution Overview
Problem
Current compression/decompression systems face challenges in achieving high computational efficiency and enhanced video quality, particularly in large-scale video processing environments, where efficient processing and encoding of large media data are critical.
Innovation Solution
The implementation of adaptive temporal and spatial filtering techniques for video pre-processing, which blend spatial and temporal filtering based on quantization parameters, global noise levels, and visual indices to reduce random fluctuations in pixel values, thereby improving video coding efficiency and quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If adaptive temporal and spatial filtering is applied to reduce random fluctuations in pixel values, then video coding efficiency and quality are improved, but computational complexity increases
Solution Approach 1:
The patent implements dynamic filtering by adapting the filtering strength and type (spatial vs. temporal) based on motion detection results and coding parameters. The system dynamically adjusts filter coefficients and selection based on real-time analysis of video content, allowing optimization of computational resources while maintaining high video quality.
Solution Approach 2:
The system changes filtering parameters such as filter strength, filter type (spatial/temporal), and filter coefficients based on quantization parameters, global noise levels, and visual indices. These parameter adjustments enable the system to achieve optimal compression efficiency at different operating conditions without excessive computational overhead.
2Productivity
If spatial and temporal filtering are blended to reduce noise, then video coding efficiency improves, but processing time increases
Solution Approach 1:
The patent segments the filtering process into distinct spatial filtering and temporal filtering stages, allowing independent optimization of each. By segmenting the processing and using motion information to determine which stage is most important, the system reduces unnecessary computations while maintaining coding efficiency.
Solution Approach 2:
The system performs preliminary motion detection and analysis before applying the full filtering process. This preliminary action determines the appropriate filter type and strength in advance, allowing the main filtering process to proceed more efficiently without trial-and-error adjustments, thus reducing overall processing time.
3Manufacturing precision
If filtering strength is increased to reduce noise, then video quality improves, but computational resources are consumed
Solution Approach 1:
The patent applies different filtering strengths and types to different regions of the video based on motion detection results. Areas with high motion content receive different filtering treatment compared to static regions, optimizing computational resource allocation while maintaining high visual quality where needed.
Solution Approach 2:
The system uses feedback from motion detection and coding parameter analysis to continuously adjust filtering strength. This feedback mechanism ensures that computational resources are allocated efficiently, applying strong filtering only when and where necessary to achieve the desired visual quality.
Data Source
AI summary
Techniques related to video pre-processing for video coding are discussed. Such video pre-processing techniques may include applying adaptive temporal and spatial filtering to pixel values of video frames of input video to generate pre-processed video such that the adaptive temporal and spatial filtering includes blending spatial and temporal filtering of the individual pixel value when the block of pixels is a non-motion block and spatial-only filtering the individual pixel value when the block of pixels is a motion block.