Adaptive Sampling Video Encoding Reducing Computation Complexity
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video coding methods are inefficient due to non-adaptive interpolation filtering, leading to increased computation resources and memory requirements, especially with high spatial resolution and frame rate video content.
Innovation Solution
A locally adaptive sampling method is introduced for video signals, determining adaptive sampling rates based on picture properties like block size and parameters, allowing for variable sampling rates within filter support areas, and applying these rates differently across block boundaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If non-adaptive interpolation filtering is used, then implementation is simple, but computation resources and memory requirements increase significantly
Solution Approach 1:
The patent applies different sampling rates to different regions of the video signal based on local complexity characteristics. Low sampling rates are applied to smooth regions while high sampling rates are applied to complex regions, optimizing the balance between computation resources and processing quality without requiring complex adaptive algorithms throughout the entire system.
Solution Approach 2:
The patent changes the sampling rate parameter based on local signal properties. By determining complexity metrics (such as gradient magnitude or variance) and adjusting the sampling rate accordingly, the system reduces computation resources in simple regions while maintaining quality in complex regions, directly addressing the contradiction between simplicity and resource consumption.
2Device complexity
If non-adaptive interpolation filtering is used, then implementation is simple, but memory requirements increase significantly
Solution Approach 1:
The patent reduces memory requirements by applying low sampling rates to large smooth regions, which requires storing and processing fewer samples. Complex regions maintain high sampling rates with corresponding memory allocations, optimizing the overall memory usage without requiring uniform high-memory allocation across the entire video signal.
Solution Approach 2:
By dynamically changing the sampling rate parameter based on local complexity, the system adjusts the quantity of samples that need to be stored in memory. This directly reduces memory requirements in simple regions while maintaining necessary memory allocation in complex regions, resolving the contradiction between implementation simplicity and memory consumption.
3Measurement precision
If uniform high sampling rate is applied, then processing quality is maintained, but computation resources and memory requirements increase
Solution Approach 1:
The patent maintains high processing quality by applying high sampling rates only to regions where it is actually needed (complex regions with high gradient magnitude or variance). Smooth regions use low sampling rates, reducing computation resources while preserving quality where it matters most. This local differentiation resolves the contradiction between maintaining quality and reducing resource consumption.
Solution Approach 2:
The sampling rate parameter is dynamically adjusted based on local complexity metrics. By changing this parameter from uniform to variable, the system maintains high measurement precision (processing quality) in complex regions while reducing computation resources in simple regions, directly resolving the contradiction between quality and resource usage.
4Measurement precision
If uniform high sampling rate is applied, then processing quality is maintained, but memory requirements increase
Solution Approach 1:
The patent maintains processing quality by applying high sampling rates only to complex regions where quality is critical. Smooth regions use low sampling rates, reducing the total quantity of samples stored in memory while maintaining quality where it matters. This spatially differentiated approach resolves the contradiction between quality maintenance and memory reduction.
Solution Approach 2:
By changing the sampling rate parameter from uniform to variable based on local complexity, the system maintains high measurement precision in complex regions while reducing the total quantity of data requiring memory storage. This parameter adaptation directly resolves the contradiction between quality and memory requirements.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Disclosed herein is a method of encoding and decoding a video signal using adaptive sampling. The method may include determining an adaptive sampling rate based on information about the properties of a decoded picture and performing interpolation filtering using samples to which the adaptive sampling rate has been applied. The adaptive sampling rate corresponds to one of a uniform sampling rate, a reduced sampling rate, and a variable sampling rate.