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We show how perceptual embeddings of the visual system can be constructed atinference-time with no training data or deep neural network features. Ourperceptual embeddings are solutions to a weighted least squares (WLS) problem,defined at the pixel-level, and solved at inference-time, that can captureglobal and local image characteristics. The distance in embedding space is usedto define a perceptual similarity metric which we call LASI: LinearAutoregressive Similarity Index. Experiments on full-reference image qualityassessment datasets show LASI performs competitively with learned deep featurebased methods like LPIPS (Zhang et al., 2018) and PIM (Bhardwaj et al., 2020),at a similar computational cost to hand-crafted methods such as MS-SSIM (Wanget al., 2003). We found that increasing the dimensionality of the embeddingspace consistently reduces the WLS loss while increasing performance onperceptual tasks, at the cost of increasing the computational complexity. LASIis fully differentiable, scales cubically with the number of embeddingdimensions, and can be parallelized at the pixel-level. A MaximumDifferentiation (MAD) competition (Wang & Simoncelli, 2008) between LASI andLPIPS shows that both methods are capable of finding failure points for theother, suggesting these metrics can be combined.

The Unreasonable Effectiveness of Linear Prediction as a Perceptual Metric