IMPROVING LEARNED INVERTIBLE CODING WITH INVERTIBLE ATTENTION AND BACK-PROJECTION

Learned image compression (LIC) is developing rapidly. Due to the high-frequency information preservation property of reversible mapping, invertible neural networks (INNs) have been applied to the transformation of LIC and successfully surpassed the latest classical coding standard Versatile Video Coding (VVC) in rate-distortion performance. However, the nonlinearity of the used INNs is limited and the dimensionality reduction method before quantization is not refined enough, respectively resulting in redundancy and information loss. We use attention instead of convolution in the coupling layers of INNs to improve information extraction while maintaining reversibility. In addition, inspired by Back-Projection (BP), we design a BP mechanism in the dimensionality adjustment to reduce information loss. Combined with the advanced channel-wise autoregressive entropy model, our model achieves significant performance improvement compared to the original model and surpasses the SOTA transformation model in the high bitrate range.