  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.
