Rethinking Latent Variable in Learned Image Compression

Benefiting from the powerful data learning and representation capabilities of neural networks, Learned Image Compression (LIC) methods have demonstrated better Rate-Distortion (RD) performance than traditional image compression frameworks. In this paper, we analyze the role of latent variables in image compression, both qualitatively and quantitatively. We then propose a latent variable compensation method to mitigate the loss introduced by quantization. We also introduce a regularization term for the latent variable mean square error into the loss function, providing more explicit guidance for the compression and reconstruction of the model's internal latent variables. Additionally, we propose a noise compensation method that acts as a plug-and-play component to enhance reconstruction in lossy image compression without significant additional encoding or decoding time. We also present a data augmentation technique involving image inversion, which helps the training set conform to the symmetry inherent in probabilistic modeling for image compression tasks. Extensive experiments demonstrate that the proposed method enhances rate-distortion metrics and visual quality.