Image Encoding Using Segmented Regions for Wearables
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Solution Overview
Problem
Mobile devices such as smartphones and digital cameras face challenges in efficiently compressing multimedia data due to limited storage, transmission bandwidth, and battery life, particularly in wearable devices like smart glasses and smartwatches, necessitating a method to enhance image compression performance without affecting image quality.
Innovation Solution
An image encoding and decoding method that utilizes device information, such as display shape and power mode, and image information, to determine non-encoding regions, block-based, and pixel-based encoding regions, employing quantization parameters for efficient entropy encoding and decoding, resulting in a bitstream that reduces data size and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If image compression is increased to reduce data size for wearable devices, then storage capacity and transmission bandwidth are improved, but image quality deteriorates
Solution Approach 1:
The image is divided into multiple regions (first region and second region) with different encoding strengths. The first region maintains high image quality with lower compression, while the second region uses higher compression. This segmentation allows the system to reduce overall data size while preserving critical image quality in important regions.
Solution Approach 2:
Different encoding parameters and compression strengths are applied to different regions of the image. The first region uses encoding parameters that preserve quality, while the second region uses more aggressive compression. This local quality approach ensures that important areas maintain fidelity while less critical areas contribute more to compression.
2Productivity
If compression performance is increased for wearable devices, then storage and transmission efficiency are improved, but power consumption increases
Solution Approach 1:
The image processing is segmented into different regions with different compression strategies. By processing only certain regions with high compression algorithms and leaving other regions with lower compression, the overall computational power required is reduced while still achieving good compression performance for the entire image.
Solution Approach 2:
Instead of applying full compression processing to the entire image, the system applies partial compression action only to specific regions (second region) where compression is most beneficial. This partial action approach reduces the total computational load and power consumption while achieving acceptable overall compression performance.
3Device complexity
If uniform encoding is applied to the entire image, then processing simplicity is maintained, but compression efficiency decreases
Solution Approach 1:
The image is segmented into different regions that are processed with different encoding parameters. This segmentation improves compression efficiency by allowing aggressive compression in suitable regions while maintaining simplicity in the overall processing framework through clear region-based rules.
Solution Approach 2:
Different encoding qualities are applied locally to different regions based on their characteristics. This local quality approach enhances overall compression efficiency without significantly increasing processing complexity, as the region classification and differential encoding follow systematic rules.
Data Source
AI summary
Encoding an image using a non-encoding region of the image, a block-based encoding region of the image, and a pixel-based encoding region of the image.