A Differentiable Entropy Model for Learned Image Compression

In an end-to-end learned image compression framework, an encoder projects the image on a low-dimensional, quantized, latent space while a decoder recovers the original image. The encoder and decoder are jointly trained with standard gradient backpropagation to minimize a rate-distortion (RD) cost function accounting for both distortions between the original and reconstructed image and the quantized latent space rate. State-of-the-art methods rely on an auxiliary neural network to estimate the rate R of the latent space. We propose a non-parametric entropy model that estimates the statistical frequencies of the quantized latent space during training. The proposed model is differentiable, so it can be plugged into the cost function to be minimized as a rate proxy and can be adapted to a given context without retraining. Our experiments show comparable performance with a learned rate estimator and better performance when is adapted over a temporal context.