Claims: 1. A method, comprising: - receiving input data divided into a plurality of blocks; - overfitting a first neural encoder network for a first block of the data based on a baseline encoder network; - encoding the first block by the first overfitted neural encoder network; - overfitting a second neural encoder network for at least one subsequent block of the data based on a combination of neural encoder networks used for previous blocks and/or the baseline encoder network; and - encoding the at least one subsequent block by the second overfitted neural encoder network. 2. The method of claim 1, further comprising: - determining which one of the overfitted neural encoder networks performs the best; and - selecting such overfitted neural encoder network for a current block. 3. The method according to claim 2, wherein the performance is determined according to one or both of the following aspects: a reconstruction quality or a bitrate. 4. A method for a neural decoder network comprising: - receiving a block residual defining a difference between an original block of data and a decoded block of the data; - based on the residual, recovering the original block to be used as ground-truth data; and - overfitting the neural decoder network based on the ground-truth data. 5. The method according to claim 4, further comprising - receiving a weight residual from a transmitter, the weight residual defining the difference between weights of the decoder before and after an overfitting. 6. An apparatus comprising at least one processor, memory including computer program code, the memory and the computer program code configured to, with the at least one processor, cause the apparatus to: