Image Interpolation via Aim-to-Mask Flag Voting
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Solution Overview
Problem
In image processing, particularly during motion compensation, it is challenging to obtain accurate motion vectors in gaps between masks, leading to irregular interpolation data and an unclean visual effect, with issues like broken masks, incorrect detection, and unclean gaps reducing video quality.
Innovation Solution
A method that determines a third aim-to-mask flag for a pixel based on the distribution of flags in its neighborhood, using an aggregate voting strategy to improve flag consistency, and performs motion optimization compensation to select appropriate interpolation data, avoiding irregularities and improving visual smoothness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If motion compensation is performed using traditional interpolation methods in gaps between masks, then processing speed is maintained, but interpolation data becomes irregular and visual quality deteriorates
Solution Approach 1:
The patent performs preliminary classification of aim-to-mask flags before motion compensation. By categorizing flags into different types (first type for mask regions, second type for non-mask regions) in advance, the system prepares the necessary information structure beforehand, enabling regular and accurate interpolation data generation during motion compensation without increasing operational complexity
Solution Approach 2:
The patent segments the processing into distinct stages: flag classification, category determination, and motion compensation. By dividing the aim-to-mask flags into different categories based on their spatial distribution and mask region characteristics, the system handles each category with appropriate processing rules, ensuring regular interpolation data while managing complexity through structured segmentation
2Reliability
If aim-to-mask flags are used directly for motion compensation, then processing efficiency is maintained, but flag inconsistency causes broken masks and unclean gaps
Solution Approach 1:
The patent implements a feedback mechanism where the classification results of aim-to-mask flags are used to guide subsequent motion compensation decisions. By analyzing the distribution patterns of flags and providing feedback on their categories, the system ensures consistent mask detection while maintaining processing efficiency through intelligent decision-making based on flag characteristics
Solution Approach 2:
The patent changes the parameter state of aim-to-mask flags by transforming them from raw binary values into categorized types with different weights and processing rules. This parameter transformation enables more reliable mask detection by considering spatial distribution patterns, while efficiency is maintained through optimized category-based processing rather than exhaustive analysis
3Manufacturing precision
If interpolation is performed without considering flag distribution, then processing simplicity is maintained, but unclean gaps and halo phenomena occur
Solution Approach 1:
The patent applies local quality principles by treating different regions with different interpolation strategies. Based on the local distribution of aim-to-mask flags, the system determines whether a region belongs to a mask or non-mask area and applies appropriate interpolation methods. This ensures smooth boundary transitions and eliminates halo phenomena by adapting the interpolation process to local characteristics without requiring complex global processing
Data Source
AI summary
A method for processing an image, an apparatus for processing an image, an electronic device, and a storage medium. The method includes: obtaining a first aim-to-mask flag of a pixel to be interpolated; obtaining second aim-to-mask flags of pixels in a preset neighborhood of the pixel to be interpolated; determining flag categories and a number corresponding to each of the flag categories, in the first aim-to-mask flag and the second aim-to-mask flags; obtaining a third aim-to-mask flag of the pixel to be interpolated based on the flag categories and the number corresponding to each of the flag categories; obtaining interpolation data of the pixel to be interpolated based on the third aim-to-mask flag of the pixel to be interpolated; and performing motion compensation on the pixel to be interpolated based on the interpolation data.