Region-Based Image Quality Control for Remote Monitoring Bandwidth
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Solution Overview
Problem
Existing techniques fail to effectively reduce the data volume of images captured from movable bodies while maintaining necessary quality for remote monitoring, particularly in scenarios where communication bandwidth is limited.
Innovation Solution
An image processing system that estimates importance levels and quality parameters for different regions of an image, allowing for optimized encoding and transmission by adjusting the quality parameters based on the estimated importance levels, thereby reducing data volume without compromising monitoring capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data is transmitted with high quality to maintain monitoring accuracy, then measurement precision is improved, but data volume increases causing transmission inefficiency
Solution Approach 1:
The patent applies local quality by dividing the image into multiple regions and assigning different quality parameters to each region based on its importance level. Important regions (e.g., areas with detected objects or regions of interest) are encoded with higher quality to maintain monitoring precision, while less important regions are encoded with lower quality to reduce overall data volume. This resolves the contradiction by making image quality non-uniform across the image, optimizing the balance between precision and data size.
Solution Approach 2:
The patent changes the quality parameter (quantization parameter) dynamically based on region importance. By adjusting the quality parameter according to detected objects and predefined importance levels, the system transmits high-quality data only where necessary for monitoring accuracy, thereby reducing total data volume while maintaining essential measurement precision in critical areas.
2Measurement precision
If uniform high quality is applied to all image regions, then measurement precision is improved, but productivity deteriorates due to increased encoding and transmission time
Solution Approach 1:
Instead of applying uniform high quality to the entire image, the patent implements local quality by encoding different regions with different quality levels matched to their importance. This significantly reduces the total amount of data that needs to be encoded and transmitted, thereby improving transmission efficiency and productivity while maintaining high quality only in regions where monitoring precision is critical.
Solution Approach 2:
The patent applies partial action by providing high-quality encoding only to important regions rather than the entire image. This partial application of high quality is sufficient to maintain monitoring precision in critical areas while avoiding the excessive data generation that would result from uniformly high-quality encoding across all regions, thus improving transmission efficiency.
3Productivity
If data volume is reduced by lowering image quality, then productivity is improved through faster transmission, but measurement precision deteriorates
Solution Approach 1:
The patent resolves this contradiction by applying local quality differentiation - important regions are encoded with high quality to preserve measurement precision for monitoring accuracy, while non-important regions use lower quality to reduce data volume. This selective approach maintains necessary precision in critical areas while achieving overall data compression for efficient transmission.
Solution Approach 2:
The system dynamically changes quality parameters based on region importance determined by object detection and predefined importance levels. This parameter adaptation ensures that quality is reduced only where it does not compromise monitoring precision, while maintaining high quality in regions where measurement accuracy is essential, thus balancing productivity and precision.
4Device complexity
If simple encoding is used to reduce processing complexity, then device complexity is reduced, but measurement precision deteriorates due to lossy compression
Solution Approach 1:
The patent employs local quality encoding where simple compression algorithms are applied uniformly across the image, but the quality parameter is locally adjusted based on region importance. This maintains relatively simple encoding device complexity while improving measurement precision in important regions through selective quality enhancement, avoiding the need for complex algorithms across the entire image.
Data Source
AI summary
Provided is an image processing apparatus which makes it possible to, during remote control, carry out suitable monitoring while suitably suppressing a transmission load. An image processing apparatus includes: an obtaining means for obtaining an image which has been captured from a movable body; an importance level estimating means for estimating levels of importance with respect to a respective plurality of regions included in the image; and a quality parameter determining means for determining quality parameters with respect to the respective plurality of regions with reference to the levels of importance.