Transformer Compressed Sensing Via Global Image Tokens

Neural Network (NN) filters improve the perceptual quality of reconstructed videos by reducing compression artefacts. For content adaptation, a few NN-filters use over-fitting. As the adaptation signal is a weight-update, compression is required to minimise significant bitrate overheads. Most approaches, however, use generic data compression algorithms, which are inadequate for coding NN weight-updates. This work introduces a content-adaptive NN post-processing filter with weight-updates coded using the Neural Network compression and Representation (NNR) standard. The bitrate overhead is further decreased by over-fitting only a subset of weights, selected via energy-based analysis. The proposed filter saved about 4.57% (Y), 10.33% (Cb), 6.53% (Cr) Bj?ntegaard Delta rate (BD-rate) on top of the Versatile Video Coding (VVC) Test Model (VTM) 11.0 with NN-based Video Coding (NNVC) 1.0, in Random Access (RA) configuration. Compared to the non-over-fitted NN, the performance was doubled; and compared to 7z, NNR reduced the bitrate of the weight-update by ?64%.