Line-Wise Image Processing Pipeline for Real-Time Super-Resolution
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Solution Overview
Problem
Existing CNN-based super-resolution methods are difficult to implement in real-time due to high computational complexity and memory requirements, making it challenging to convert low-resolution images to high-resolution images efficiently, especially in low-complexity hardware.
Innovation Solution
The proposed image processing device and method utilize a line-wise operation with a pipeline architecture, incorporating a residual block and depth-wise separable convolution, along with feature map compression and quantization, to reduce memory usage and computational load, enabling efficient conversion of 2K FHD to 4K UHD images in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CNN-based super-resolution method is used, then image quality is improved, but computational complexity increases
Solution Approach 1:
The patent segments the image processing into line-wise operations, processing one scan line at a time instead of the entire image. This divides the computational task into smaller, manageable units that can be processed sequentially with minimal memory requirements, resolving the contradiction between maintaining image quality and reducing computational complexity.
Solution Approach 2:
The patent extracts and processes only the essential features needed for super-resolution by using a simplified neural network architecture that operates on individual lines. This extraction approach maintains the core functionality of CNN-based super-resolution while removing unnecessary computational overhead, thereby improving image quality without proportionally increasing complexity.
2Measurement precision
If CNN-based super-resolution method is used, then image quality is improved, but memory requirements increase
Solution Approach 1:
By segmenting the processing into line-wise operations, the patent reduces memory requirements from needing to store entire images to storing only single scan lines or small buffers. This segmentation allows high-quality super-resolution processing with minimal memory, directly resolving the contradiction between image quality and memory requirements.
Solution Approach 2:
The patent applies local quality processing by focusing computational resources on processing each line independently with appropriate buffer sizes. This local approach maintains overall image quality while significantly reducing the total memory required compared to global image processing methods.
3Measurement precision
If accurate CNN-based SR method is implemented, then visual quality is improved, but hardware complexity increases
Solution Approach 1:
The patent implements hardware-friendly segmentation by processing images line-by-line through a pipeline architecture. This segmentation allows the hardware to process super-resolution tasks using simple, repeatable operations on small data units, achieving accurate visual quality without requiring complex hardware structures.
Solution Approach 2:
The patent introduces dynamic elements through its pipeline architecture that can adapt to different processing requirements. The line-wise processing pipeline dynamically manages data flow and computation, enabling accurate CNN-based super-resolution in hardware while maintaining flexibility and avoiding fixed complex structures.
4Measurement precision
If learning-based SR algorithm is used, then reconstruction accuracy is improved, but implementation difficulty increases
Solution Approach 1:
The patent simplifies implementation by segmenting the learning-based super-resolution process into line-wise operations with fixed buffer sizes. This segmentation transforms the complex learning algorithm into a series of simple, repeatable steps that are easier to manufacture and implement in hardware while maintaining reconstruction accuracy.
Solution Approach 2:
The patent changes key parameters from processing entire images to processing individual lines with controlled buffer depths. This parameter change simplifies the implementation of learning-based SR algorithms by reducing the state space and making the system more manageable for manufacturing while preserving reconstruction accuracy through the learned models.
Data Source
AI summary
Disclosed are an image processing method and device using a line-wise operation. The image processing device, according to one embodiment, comprises: a receiver for receiving an image; at least one first line buffer for outputting the image into a line-wise image line; a first convolution operator for generating a feature map by performing a convolution operation on the basis of the output from the first line buffer; and a feature map processor for storing the output from the first convolution operator in units of at least one line, and processing so as to output the feature map stored in units of at least one line into a two-dimensional form, wherein at least one convolution operation operates in the form of a pipeline.