Image Processing Apparatus Reducing Smear Without Brightness Loss
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Solution Overview
Problem
Current display technologies, such as TFT-LCDs, suffer from smear and blurring when displaying moving images due to liquid crystal response speed and human vision afterglow effects, with existing solutions like black frame insertion and motion estimation causing brightness loss and complex algorithms.
Innovation Solution
An image processing method that extracts a feature element causing smear from adjacent frames, generates a reconstructed frame without this element, and inserts it between original frames, using subtraction and morphological operations to reduce brightness of high-brightness pixels, and performs frequency doubling to minimize brightness loss and improve energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If black frame insertion is used to reduce afterglow, then smear is reduced, but brightness loss occurs
Solution Approach 1:
Instead of applying black frame insertion globally to the entire display, the patent applies the technique locally only to regions containing moving objects. By identifying moving object regions through frame comparison and applying afterglow reduction only in these areas, the patent eliminates smear for moving objects while preserving brightness in static regions, thus resolving the contradiction between smear reduction and brightness maintenance.
Solution Approach 2:
The patent segments the display area into moving object regions and static regions based on frame-to-frame comparison. Different processing strategies are applied to different segments: afterglow reduction is applied to moving object segments while standard display is maintained for static segments. This segmentation allows selective application of the harmful factor reduction technique, minimizing overall brightness loss.
2Object-affected harmful factors
If motion estimation and compensation is used to reduce smear, then moving image quality is improved, but algorithm complexity increases
Solution Approach 1:
The patent extracts only the essential information needed for afterglow reduction by comparing frames to identify moving object regions. Instead of performing full motion estimation and compensation algorithms, it extracts moving object masks through simple frame subtraction and logical operations. This extraction approach achieves smear reduction with significantly reduced algorithmic complexity compared to traditional motion estimation methods.
Solution Approach 2:
The patent uses simple, computationally inexpensive operations (frame subtraction, logical AND/OR operations) instead of complex motion estimation algorithms. These simple operations are performed on each frame pair to generate moving object masks, which are then used for selective afterglow reduction. The approach trades off using multiple simple operations for one complex algorithm, achieving the same goal with lower overall complexity.
3Object-affected harmful factors
If frame rate is increased to reduce afterglow effect, then smear is reduced, but power consumption increases
Solution Approach 1:
The patent implements periodic afterglow reduction by inserting black frames at specific intervals only in moving object regions. Instead of continuously displaying at higher frame rates, it uses periodic black frame insertion synchronized with the display refresh cycle. This periodic action reduces afterglow accumulation while maintaining lower average power consumption compared to continuous high frame rate display.
Solution Approach 2:
The patent applies higher effective frame rate processing only locally to moving object regions where afterglow is problematic. In static regions, the display operates at the base frame rate, consuming less power. This localized application of high frame rate techniques reduces overall power consumption while still achieving smear reduction where needed.
Data Source
AI summary
An image processing method provided by embodiments of the present disclosure includes acquiring, two adjacent frames of original images from a video image data stream; extracting a feature element that produces smear from the two adjacent frames of the original images; generating a reconstructed image frame that does not comprise the feature element by using the two adjacent frames of the original images and the feature element; and inserting the reconstructed image frame between the two adjacent frames of the original images.