Spatially adaptive image compression using a tiled deep network
Abstract
Deep neural networks represent a powerful class of function approximators that
can learn to compress and reconstruct images. Existing image compression
algorithms based on neural networks learn quantized representations with a
constant spatial bit rate across each image. While entropy coding introduces
some spatial variation, traditional codecs have benefited significantly by
explicitly adapting the bit rate based on local image complexity and visual
saliency. This paper introduces an algorithm that combines deep neural
networks with quality-sensitive bit rate adaptation using a tiled network. We
demonstrate the importance of spatial context prediction and show improved
quantitative (PSNR) and qualitative (subjective rater assessment) results
compared to a non-adaptive baseline and a recently published image compression
model based on fully-convolutional neural networks.
can learn to compress and reconstruct images. Existing image compression
algorithms based on neural networks learn quantized representations with a
constant spatial bit rate across each image. While entropy coding introduces
some spatial variation, traditional codecs have benefited significantly by
explicitly adapting the bit rate based on local image complexity and visual
saliency. This paper introduces an algorithm that combines deep neural
networks with quality-sensitive bit rate adaptation using a tiled network. We
demonstrate the importance of spatial context prediction and show improved
quantitative (PSNR) and qualitative (subjective rater assessment) results
compared to a non-adaptive baseline and a recently published image compression
model based on fully-convolutional neural networks.
Research Areas
