Designing Visualization Systems Based on Information Theory

The BAIR (Berkeley AI) Laboratory introduced a new approach to designing imaging systems based on direct measurement of information content. In many systems—from smartphones to MRIs and lidars—measurements are not intended for human perception, but AI can extract useful information. Traditional quality metrics (resolution, signal-to-noise ratio) evaluate factors separately, while training neural networks for reconstruction or classification mixes the quality of hardware and algorithms. The authors proposed evaluating the mutual information between the object and the measurement using only noisy data and a known noise model (Poisson photon noise and Gaussian read noise). This allows decomposing the estimate into two components: entropy of measurements (estimated by a probabilistic model, e.g., a transformer) and conditional entropy (computed analytically). The method was tested in four domains: color photography (Bayer filter selection), radio astronomy (telescope placement), lensless imaging, and microscopy. In all cases, the information estimate correlated with reconstruction quality by neural networks. Based on this approach, a method called IDEAL (Information-Driven Encoder Analysis Learning) was developed, which optimizes imaging system parameters (e.g., color filter) via gradient ascent on the information estimate without requiring decoder training. Results showed that IDEAL achieves the same quality as end-to-end training but with lower memory and computational cost. According to the authors, the method can be applied not only in imaging but also in other sensor types—electronic, biological, chemical.