Measuring Imaging Quality Through Information Content: A New Framework
Introduction: Beyond Human-Readable Images
Modern imaging systems often produce data that never reaches human eyes in its raw form. Your smartphone applies complex algorithms to sensor data before you see a photo. MRI scanners collect frequency-space measurements that require reconstruction before doctors can diagnose. Self-driving cars process camera and LiDAR data directly with neural networks, never displaying the intermediate signals. What matters in these systems is not how the measurements look, but how much useful information they contain. Artificial intelligence can extract this information even when it is encoded in ways humans cannot interpret.
Yet we rarely evaluate information content directly. Traditional metrics like resolution and signal-to-noise ratio assess individual aspects of quality separately, making it difficult to compare systems that trade off between these factors. The common alternative—training neural networks to reconstruct or classify images—conflates the quality of the imaging hardware with the quality of the algorithm. This creates a need for a direct, hardware-agnostic measure of imaging performance.
Why Mutual Information?
Mutual information quantifies how much a measurement reduces uncertainty about the object that produced it. Two systems with the same mutual information are equivalent in their ability to distinguish objects, even if their measurements look completely different. This single number captures the combined effect of resolution, noise, sampling, and all other factors that affect measurement quality. A blurry, noisy image that preserves the features needed to distinguish objects can contain more information than a sharp, clean image that loses those features.
Information unifies traditionally separate quality metrics. It accounts for noise, resolution, and spectral sensitivity together rather than treating them as independent factors. For example, a low-resolution but high-dynamic-range sensor might outperform a high-resolution sensor that clips highlights, and mutual information will reflect that trade-off.
Previous Attempts and Their Limitations
Earlier efforts to apply information theory to imaging faced two significant problems. The first approach treated imaging systems as unconstrained communication channels, ignoring the physical limitations of lenses and sensors. This produced wildly inaccurate estimates. The second approach required explicit models of the objects being imaged, limiting generality—if your model of the object is wrong, the information estimate is useless.
Our method avoids both problems by estimating information directly from measurements, as described in our NeurIPS 2025 paper.
Our Approach: Estimating Information from Measurements
We developed a framework that enables direct evaluation and optimization of imaging systems based on their information content. The key insight is to use only the noisy measurements and a noise model to quantify how well measurements distinguish objects. This bypasses the need for explicit object models and avoids the errors of treating optics as unconstrained.
The estimator works by computing mutual information between the measurement and the underlying object, using the noise model to account for corruption. This approach is computationally efficient because it leverages the noise structure rather than requiring complex sampling.
How It Works
- Input: Noisy measurements from an optical system (e.g., camera sensor counts, MRI k-space lines).
- Noise model: A mathematical description of the noise process (e.g., Poisson for photon counting, Gaussian for thermal noise).
- Output: A single number—the mutual information—that represents the system's ability to distinguish objects.
This information metric can be used to compare different hardware designs without needing to retrain algorithms. It also serves as an objective for end-to-end optimization, where the design of optics and algorithms are tuned jointly.
Results and Implications
In our NeurIPS 2025 paper, we show that this information metric predicts system performance across four imaging domains: microscopy, photography, medical imaging, and autonomous driving sensors. Optimizing the metric produces designs that match state-of-the-art end-to-end methods while requiring less memory, less compute, and no task-specific decoder design.
This has practical implications for imaging system design:
- Faster prototyping: Engineers can use mutual information to evaluate designs without building and testing full prototype systems.
- Hardware-agnostic comparison: Different sensor technologies (e.g., CCD vs. CMOS) can be compared on an equal footing.
- Joint optimization: Optics and processing algorithms can be co-designed to maximize information flow.
Conclusion
Direct information estimation offers a principled way to evaluate and optimize imaging systems. By focusing on what matters—how well measurements distinguish objects—we can design cameras, sensors, and medical imagers that perform better for AI-based analysis. This framework bridges the gap between information theory and practical imaging, enabling the next generation of intelligent vision systems.
For more details, see the full NeurIPS 2025 paper and our open-source implementation.