Wide-Angle Image Compression via Region-Based Rate Adjustment
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Solution Overview
Problem
Existing image data compression methods do not adequately consider the unique characteristics of wide-angle cameras, resulting in insufficient high-image-quality, high-efficiency compression.
Innovation Solution
An image processing apparatus that divides captured images into regions and adjusts the compression rate based on the angle from the optical axis and distance from the camera, applying different compression rates to regions with varying distortion and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a uniform compression rate is applied to the entire captured image, then the processing is simple and fast, but the image quality is degraded in regions with low distortion and the compression efficiency is insufficient in regions with high distortion
Solution Approach 1:
The captured image is divided into multiple regions based on distortion characteristics, with each region processed using an appropriate compression rate. This segmentation allows high compression rates to be applied to peripheral regions with high distortion while maintaining lower compression rates for central regions with low distortion, thereby improving overall compression efficiency without uniformly degrading image quality.
Solution Approach 2:
Different compression rates are applied to different regions of the image according to their local distortion characteristics. The central region with low distortion receives a lower compression rate to preserve image quality, while peripheral regions with high distortion receive higher compression rates. This local quality approach optimizes the balance between compression efficiency and image quality preservation.
2Quantity of substance
If a high compression rate is applied to the entire image, then the data amount is reduced, but the image quality is significantly degraded
Solution Approach 1:
The image is segmented into regions with different distortion characteristics, allowing differential compression rate application. This enables significant data reduction in peripheral regions with high distortion while preserving image quality in central regions with low distortion, achieving an optimal balance between data amount reduction and image quality maintenance.
Solution Approach 2:
Different compression rates are applied locally to different image regions based on their distortion characteristics. Regions with high distortion tolerate higher compression rates with minimal quality impact, while regions with low distortion receive lower compression rates to preserve quality. This local quality strategy achieves effective data reduction without uniform quality degradation.
3Manufacturing precision
If a low compression rate is applied to the entire image, then the image quality is maintained, but the data amount is not sufficiently reduced
Solution Approach 1:
The image is divided into regions with different distortion characteristics, allowing the application of appropriately differentiated compression rates. This segmentation enables significant data reduction in peripheral regions with high distortion where quality degradation is less noticeable, while maintaining lower compression rates in central regions to preserve image quality, thereby achieving effective data amount reduction without uniform quality compromise.
Solution Approach 2:
Different compression rates are applied to different local regions based on their distortion characteristics. Peripheral regions with high distortion receive higher compression rates, accepting some quality degradation, while central regions with low distortion receive lower compression rates to maintain quality. This local quality approach achieves effective data reduction while preserving overall image quality.
4Productivity
If conventional compression methods are used without considering camera characteristics, then the processing is straightforward, but the compression efficiency is insufficient for wide-angle camera images
Solution Approach 1:
The compression processing is adapted to the local characteristics of wide-angle camera images by applying different compression rates to different regions based on their distortion characteristics. This local quality approach recognizes that peripheral regions of wide-angle images have different quality requirements compared to central regions, thereby improving compression efficiency specifically for wide-angle camera characteristics.
Solution Approach 2:
The compression rate parameter is changed according to the spatial position and distortion characteristics within the image. By dynamically adjusting the compression rate parameter based on local image characteristics, the system achieves higher compression efficiency for wide-angle camera images while maintaining appropriate image quality in different regions.
Data Source
AI summary
An image processing device comprises a region dividing unit which divides a captured image obtained by a camera unit into a plurality of regions, and an image compressing unit which compresses each of region images obtained by the division by the region dividing unit while changing the compression rate according to the distance from a predetermined point in the captured image to each of the region images and the distance from the camera unit to a target included in each of the regions. For example, the image compressing unit compresses the region image having a larger angle from the optical axis at a larger compression rate, thereby achieving compression in which the amount of data in low-quality regions is greatly reduced and the quality of high-quality regions is maintained.