Variable-Resolution Image Encoding for Bandwidth-Limited Sensors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current imaging systems face challenges in efficiently transmitting video images with varying resolutions, as they either require high bandwidth for high-resolution images or low-resolution images, without the capability to decode both entire images and desired regions simultaneously, leading to significant bandwidth usage.
Innovation Solution
The implementation of a variable resolution image format (VRIF) encoding system that divides images into regions of different resolutions, where high-resolution areas are prioritized and transmitted alongside low-resolution areas, using superpixels and downsampling logic to reduce bandwidth by only transmitting essential high-resolution regions and compressing less important parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are transmitted in their entirety, then image quality is improved, but bandwidth requirements increase significantly
Solution Approach 1:
The image is divided into multiple regions of interest (ROIs) with different importance levels. Each ROI is processed and transmitted at different resolutions, allowing the system to focus bandwidth on critical areas while reducing transmission data for less important regions, thus resolving the contradiction between image quality and bandwidth requirements.
Solution Approach 2:
Different regions of the image are assigned different quality levels based on their importance. High-importance regions maintain high resolution while low-importance regions are transmitted at lower resolution. This local differentiation allows the system to preserve essential image quality while significantly reducing overall bandwidth consumption.
2Quantity of substance
If low-resolution images are transmitted, then bandwidth requirements are reduced, but image quality deteriorates
Solution Approach 1:
The image is segmented into regions of interest and non-interest areas. By transmitting only essential high-resolution ROI data and compressing non-ROI areas, the system achieves lower overall bandwidth requirements while maintaining adequate image quality in critical regions, thus resolving the contradiction between bandwidth reduction and quality preservation.
Solution Approach 2:
The system applies different quality levels locally across the image - high quality for important regions and lower quality for less important regions. This approach reduces overall bandwidth requirements while ensuring that essential image quality is maintained where needed, effectively resolving the contradiction.
3Loss of information
If the entire image is transmitted at high resolution, then complete image information is preserved, but decoding capability for both entire image and specific regions simultaneously is not achieved
Solution Approach 1:
The image data is organized into segmented regions with different resolution levels. The encoding structure includes metadata that identifies ROI boundaries and resolution levels, enabling the decoder to efficiently process and display both the complete variable-resolution image and specific high-resolution regions simultaneously without requiring full high-resolution data for the entire image, thus reducing decoding complexity while preserving essential information.
Solution Approach 2:
The system dynamically adjusts resolution allocation based on region importance rather than applying a static uniform resolution. This dynamic approach allows the decoder to handle variable resolution data efficiently, preserving complete image information where needed while reducing complexity through selective high-resolution processing only in critical regions.
4Device complexity
If uniform resolution is applied to the entire image, then encoding simplicity is maintained, but bandwidth efficiency is reduced
Solution Approach 1:
The encoding process segments the image into regions of interest with associated importance levels. This segmentation adds structured metadata that guides the encoding process, allowing the system to apply different compression and resolution strategies to different regions. The result is improved bandwidth efficiency while maintaining manageable encoding complexity through systematic region-based processing.
Solution Approach 2:
The system applies local quality differentiation by encoding different regions at different resolutions based on their importance. This approach improves bandwidth efficiency by allocating transmission resources according to regional significance rather than applying uniform encoding throughout the entire image, achieving better bandwidth efficiency without excessive encoding complexity.
Data Source
AI summary
A system is provided that is configured to encode an image in accordance with a variable resolution image format. The variable resolution image format allows the specification of a number of windows in terms of their location and resolution. The image can be decomposed into a minimum number of square superpixels such that all specified windows are at the assigned resolution or better. By encoding one image where only critical portions are at the high resolution while less critical portions are at intermediate or lower resolutions, the number of bits that need to be transmitted from the system to a remote host subsystem can be dramatically reduced.