Content Adaptive Scaler Using Farrow Structure
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Solution Overview
Problem
Existing scaling technologies in video processing systems suffer from complex hardware structures and fail to effectively reduce undesirable artifacts such as saw tooth phenomenon, edge blurring, and moiré artifacts during up-conversion and down-conversion processes.
Innovation Solution
A content adaptive scaler using a parametric Farrow structure with a digital filter that adjusts filtering coefficients based on detected differences between input pixels, employing a parametric Farrow structure with an overshoot control module to generate output pixels with minimal artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional interpolation and decimation techniques are used for scaling, then scaling operation can be performed, but undesirable artifacts such as saw tooth phenomenon, edge blurring, ringing and moiré artifacts are generated
Solution Approach 1:
The patent applies dynamics by making the filter coefficients adaptive rather than fixed. The coefficients are dynamically adjusted based on the local image content characteristics (such as edge strength and frequency content) at different positions during the scaling operation. This allows the filtering process to adapt to varying image conditions and reduce artifacts like saw tooth phenomenon, edge blurring, ringing and moiré patterns while maintaining scaling productivity.
Solution Approach 2:
The patent changes the parameters of the filtering process by introducing content-adaptive coefficient adjustment. Instead of using constant filter coefficients, the system modifies the filtering parameters based on detected image content characteristics at each position, thereby optimizing the scaling process and minimizing harmful artifacts such as saw tooth phenomenon, edge blurring, ringing and moiré artifacts.
2Object-affected harmful factors
If complex hardware structures are used to reduce artifacts, then artifact reduction may be improved, but hardware complexity increases
Solution Approach 1:
The patent reduces hardware complexity by implementing artifact reduction through parameter changes in the filtering process. Instead of using complex hardware structures, the system adjusts filter coefficients based on image content characteristics, achieving artifact reduction (saw tooth phenomenon, edge blurring, ringing and moiré artifacts) through software-based adaptive filtering rather than hardware complexity.
Solution Approach 2:
The patent substitutes complex hardware filtering structures with a content-adaptive algorithmic approach. By replacing mechanical/computational complexity with intelligent parameter adjustment based on image content analysis, the system achieves artifact reduction without requiring complex hardware implementations.
3Productivity
If 2-dimentional filtering is applied for scaling, then scaling can be performed, but image content deteriorates along one dimension
Solution Approach 1:
The patent applies local quality by analyzing and adjusting filtering parameters independently at different positions in the image based on local content characteristics. Each position is processed with customized filter coefficients adapted to the local image content (such as edge strength and frequency), preventing the deterioration of image quality along any particular dimension while maintaining overall scaling productivity.
Data Source
AI summary
Embodiments of the present invention are directed to an image processing system. The image processing system may comprise a content detection module having an input to receive a sequence of input pixels and configured to generate an adjustable parameter based on detected differences between adjacent pairs of input pixels, and a digital filter having an input for the sequence of input pixels and a control input coupled to an output of the content detection module. The digital filter may adjust filtering coefficients according to the parameter.