LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

TL;DR

This paper introduces LVQAC, a novel lattice vector quantization with adaptive companding, significantly improving rate-distortion performance in learned image compression systems while maintaining computational efficiency.

Contribution

The paper proposes a new LVQAC scheme that combines lattice vector quantization with adaptive companding, enhancing efficiency and adaptability over traditional uniform scalar quantizers.

Findings

LVQAC outperforms uniform quantizers in rate-distortion metrics.

LVQAC maintains low computational complexity.

Integration of LVQAC improves existing CNN-based image compression models.

Abstract

Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear analysis and synthesis transforms that can be facilitated by deep neural networks. However, out of operational expediency, most of these end-to-end methods adopt uniform scalar quantizers rather than vector quantizers, which are information-theoretically optimal. In this paper, we present a novel Lattice Vector Quantization scheme coupled with a spatially Adaptive Companding (LVQAC) mapping. LVQ can better exploit the inter-feature dependencies than scalar uniform quantization while being computationally almost as simple as the latter. Moreover, to improve the adaptability of LVQ to source statistics, we couple a spatially adaptive companding (AC) mapping with LVQ. The resulting LVQAC design can be easily embedded into any end-to-end optimized image compression system. Extensive experiments demonstrate that for any end-to-end CNN image compression models, replacing uniform quantizer by LVQAC achieves better rate-distortion performance without significantly increasing the model complexity.