High Perceptual Quality Wireless Image Delivery with Denoising Diffusion
  Models
Selim F. Yilmaz Xueyan Niu Bo Bai Wei Han Lei Deng Deniz Gunduz
We consider the image transmission problem over a noisy wireless channel via
deep learning-based joint source-channel coding (DeepJSCC) along with a
denoising diffusion probabilistic model (DDPM) at the receiver. Specifically,
we are interested in the perception-distortion trade-off in the practical
finite block length regime, in which separate source and channel coding can be
highly suboptimal. We introduce a novel scheme that utilizes the range-null
space decomposition of the target image. We transmit the range-space of the
image after encoding and employ DDPM to progressively refine its null space
contents. Through extensive experiments, we demonstrate significant
improvements in distortion and perceptual quality of reconstructed images
compared to standard DeepJSCC and the state-of-the-art generative
learning-based method. We will publicly share our source code to facilitate
further research and reproducibility.