Encoding Image Metadata in Least Significant Bits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional techniques for appending image metrics to captured image data in solid-state imaging devices are sub-optimal, particularly for higher-resolution engines, as they hinder frame rates and result in undesirable visible binary data, making it difficult to scale and transmit effectively.
Innovation Solution
Encoding image metadata into the least significant bits of pixel data within the image data, allowing for invisible metadata transport to the host processor for processing, using a steganographic process that segments image data into pixel groupings and determines metadata for each grouping, which is then encoded and transmitted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If image metrics are appended to the end of image data, then the main processor can access image metrics, but the image data size increases and transmission time increases, resulting in lower frame rates
Solution Approach 1:
The patent merges image metrics with image data by encoding the metrics directly into the image data structure. Specifically, the scan engine encodes image metrics (such as maximum pixel intensity, minimum pixel intensity, and pixel intensity range) into the least significant bits of the image data, creating a unified data structure that contains both image information and metric information without requiring separate transmission channels.
Solution Approach 2:
The patent implements a nested structure where image metrics are embedded within the image data. The metrics are nested at multiple hierarchical levels: pixel-level metrics are embedded in pixel groups, which are then embedded in larger image blocks. This nested organization allows the main processor to access metrics at appropriate granularities without processing the entire image, improving processing efficiency while maintaining frame rates.
2Loss of information
If image metrics are appended to image data, then the main processor can use the metrics for processing, but the appended binary data becomes visible in the image, which is undesirable
Solution Approach 1:
The patent applies local quality by encoding image metrics into specific localized regions of the image data rather than appending them globally. The metrics are embedded in the least significant bits of selected pixel groups, which are strategically chosen regions that minimize visual impact. This localized encoding ensures that the metrics are accessible while the visual quality of the image remains preserved, as the encoded regions are imperceptible to human vision.
3Loss of information
If extra columns or rows are added to accommodate image metrics in higher-resolution images, then the metrics can be stored, but the image becomes very large and transmission time increases
Solution Approach 1:
The patent applies partial action by encoding only the essential image metrics rather than transmitting complete pixel data for metric calculation. The scan engine calculates and encodes key metrics (maximum intensity, minimum intensity, intensity range) for pixel groups, which provides sufficient information for the main processor to perform decoding operations without requiring the full high-resolution image data. This partial transmission approach significantly reduces data size while maintaining processing effectiveness.
Data Source
AI summary
Systems and methods for encoding metadata in image data captured by an imaging device, such as a barcode device or machine vision device, are provided. An example method includes analyzing raw image data at a front-end applicant specific integrated circuit to determine image metadata for each of a plurality of different pixel groupings collectively forming the raw image data. A least significant bit process is then used to encode the metadata into the image data, in a manner visually hidden from a user. A host processor receives the encoded image data, decodes the image metadata and uses that to process the image data, for example, performing barcode decoding or machine vision processes.