Region-Based Image Filtering for Better Compression Quality
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Solution Overview
Problem
Conventional image processing methods apply filtering to entire image frames without considering regional characteristics, leading to reduced image quality and inefficient resource usage, as well as unsatisfactory compression results.
Innovation Solution
An encoding apparatus and method that select an optimum region division mode for an image frame, transmit this mode to a decoding apparatus, and apply independent filtering to each region using an optimum filtering method and coefficient, based on cost functions, to improve image processing results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If filtering is applied to the entire image frame without considering regional characteristics, then the processing is simple and fast, but the image quality is reduced and resources are used inefficiently
Solution Approach 1:
The image frame is divided into multiple regions with different characteristics (e.g., smooth regions, edge regions, texture regions). Each region is processed independently with appropriate filtering strategies, allowing quality optimization for each region while managing overall processing complexity through systematic division.
Solution Approach 2:
Different filtering methods and parameters are applied to different regions based on their local characteristics. For example, stronger filtering is applied to smooth regions while preserving edges in edge regions, and maintaining texture details in texture regions. This local adaptation improves overall image quality without uniformly increasing processing complexity across the entire frame.
2Productivity
If filtering is applied to the entire image frame, then processing is uniform and simple, but excessive resources are used
Solution Approach 1:
The image frame is segmented into regions with similar characteristics, allowing the system to apply filtering only where necessary and with appropriate intensity. This reduces redundant processing in regions that don't require strong filtering, thereby improving processing efficiency while reducing resource consumption.
Solution Approach 2:
Instead of applying full-strength filtering uniformly across the entire image frame, the system applies partial or selective filtering only to regions that benefit from it. This partial action approach maintains productivity by processing only necessary regions while significantly reducing overall resource consumption.
3Manufacturing precision
If filtering is applied to the entire image frame, then processing is straightforward, but compression effectiveness is unsatisfactory
Solution Approach 1:
The image frame is divided into regions with different characteristics (smooth, edge, texture regions). Each region is compressed with parameters optimized for its specific characteristics, improving overall compression effectiveness. The segmentation allows the system to achieve better compression ratios by adapting to local image features rather than applying uniform compression.
Solution Approach 2:
Different compression parameters and filtering strategies are applied to different regions based on their local characteristics. Smooth regions receive stronger compression and filtering, while edge and texture regions receive milder processing to preserve important visual information. This local adaptation improves compression effectiveness without requiring overly complex processing.
Data Source
AI summary
Region-based encoding apparatus and decoding apparatus. The encoding apparatus selects an optimum region division mode from region division modes with respect to regions divided from an image frame, and transmits, to the decoding apparatus, an optimum image filtering method and an optimum filter coefficient of regions divided, according to the optimum region division mode.