Adaptive Image Encoding with Region of Importance
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Solution Overview
Problem
Image compression technologies face interruptions in wireless communication environments due to varying signal strength, leading to inefficient data transmission and potential loss of image quality.
Innovation Solution
An adaptive image encoding method that extracts a region of importance (ROI) and determines a compression rate for the remaining region based on network state, encoding the ROI and remaining region separately to maintain high image quality and control data transmission effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform compression is applied to the entire image, then data transmission efficiency is improved, but image quality in important regions deteriorates
Solution Approach 1:
The patent applies different compression rates to different regions of the image based on their importance. The ROI (Region of Interest) is identified and assigned a lower compression rate to preserve quality, while non-ROI areas receive higher compression rates to reduce data size. This resolves the contradiction by making compression quality spatially variable rather than uniform.
Solution Approach 2:
The image is divided into multiple regions: ROI (Region of Interest) and non-ROI areas. This segmentation allows the system to apply different encoding strategies to different parts of the image, enabling high-quality transmission of important regions while compressing less critical areas more aggressively.
2Quantity of substance
If high compression rate is used to reduce data size, then transmission bandwidth is improved, but image quality and reliability deteriorate
Solution Approach 1:
Different compression rates are applied to different regions based on their importance. Critical ROI areas use lower compression rates to maintain quality and reliability, while non-critical areas use higher compression rates to reduce overall data size. This selective approach resolves the contradiction between data size reduction and quality preservation.
3Adaptability or versatility
If adaptive compression based on network state is implemented, then transmission adaptability is improved, but encoding complexity increases
Solution Approach 1:
The compression rate is dynamically adjusted based on network conditions (signal strength, bandwidth availability). The encoder monitors network state and adapts the compression parameters in real-time, allowing the system to optimize transmission performance according to current conditions while managing encoding complexity through adaptive control.
Solution Approach 2:
The system performs preliminary identification of ROI regions before compression encoding. By pre-segmenting the image and marking important regions, the system simplifies the subsequent adaptive compression process, as the encoder only needs to adjust compression rates for already-identified regions rather than making complex decisions during encoding.
Data Source
AI summary
An image encoding apparatus and method and an image decoding apparatus and method are disclosed. The image encoding method may include extracting a ROI from an input image, determining a compression rate of a remaining region excluding the ROI of the image based on a network state between the image transmitting apparatus and the image receiving apparatus, and encoding the remaining region based on the compression rate.