Camera Image Processing Using Luminance-Based Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Real-time image and video encoders in digital camera systems face issues with lossy content encoding, leading to image quality loss and data variance, while also consuming higher bandwidth and power than typical cameras can provide.
Innovation Solution
A camera system that includes an image sensor, a processing apparatus, and a non-transitory computer-readable medium with instructions to convert raw image data into color-space data, calculate luminance levels, determine quantization levels based on these levels, and encode the data to produce encoded image data, thereby optimizing image processing based on luminance and activity data without significantly increasing bandwidth or power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time encoding is applied to improve processing speed, then productivity is improved, but image quality deteriorates due to lossy compression
Solution Approach 1:
The patent applies different quantization levels to different regions of the image based on local luminance characteristics. High-luminance regions receive lower quantization levels (better quality) while low-luminance regions receive higher quantization levels (lower quality), optimizing the balance between overall image quality and encoding speed.
Solution Approach 2:
The patent dynamically adjusts quantization parameters based on luminance data. By changing the quantization level parameter according to local luminance conditions, the system achieves adaptive compression that maintains quality where needed while enabling faster encoding in less critical areas.
2Loss of energy
If higher compression is applied to reduce bandwidth, then loss of information is reduced, but image quality deteriorates
Solution Approach 1:
Different compression strengths are applied to different image regions based on luminance. Bright regions are compressed less (preserving quality) while dark regions are compressed more (reducing bandwidth), achieving efficient bandwidth utilization without uniform quality loss.
Solution Approach 2:
The quantization parameter is dynamically changed based on luminance measurements. This parameter adaptation allows the system to optimize the trade-off between compression ratio and quality preservation, reducing bandwidth consumption while maintaining perceptual image quality.
3Manufacturing precision
If advanced encoding algorithms are used to improve image quality, then manufacturing precision is improved, but use of energy increases
Solution Approach 1:
The patent uses luminance data to adaptively adjust quantization parameters, replacing more complex quality-optimization algorithms. This parameter-based approach achieves good image quality with lower computational complexity and reduced power consumption compared to advanced encoding algorithms.
Data Source
AI summary
A camera system processes images based on image luminance data. The camera system includes an image sensor, an image pipeline, an encoder and a memory. The image sensor converts light incident upon the image sensor into raw image data. The image pipeline converts raw image data into color-space image data and calculates luminance levels of the color-space image data. The encoder can determine one or more of quantization levels, determining GOP structure or reference frame spacing for the color-space image data based on the luminance levels. The memory stores the color-space image data and the luminance levels.