A learned, orthogonality-constrained lattice codebook, optimized end-to-end with Babai rounding and a factored Gaussian mixture rate model, improves rate-distortion performance of neural image codecs over uniform scalar quantization.
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations
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abstract
In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations. Our method, termed LL-VQ-VAE, replaces the vector quantization layer in VQ-VAE with lattice-based discretization. The learnable lattice imposes a structure over all discrete embeddings, acting as a deterrent against codebook collapse, leading to high codebook utilization. Compared to VQ-VAE, our method obtains lower reconstruction errors under the same training conditions, trains in a fraction of the time, and with a constant number of parameters (equal to the embedding dimension $D$), making it a very scalable approach. We demonstrate these results on the FFHQ-1024 dataset and include FashionMNIST and Celeb-A.
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Learning Optimal Lattice Vector Quantizers for End-to-end Neural Image Compression
A learned, orthogonality-constrained lattice codebook, optimized end-to-end with Babai rounding and a factored Gaussian mixture rate model, improves rate-distortion performance of neural image codecs over uniform scalar quantization.