neural networks overfitted to those neighboring blocks may be even farther away in weight space than the baseline mode or previously overfitted neural networks. Thus, the transmitter may apply an evaluation phase where the best neural network for the start is selected. This can be made for example by running parallel overfitting sessions from different neural networks used as a start, such as from the “neighboring overfitted neural networks”(the neural networks overfitted on neighboring blocks), from the baseline network, and optionally from any other previously-overfitted neural network. After the multiple overfitting sessions are completed, the neural network performing best on the current block is selected, for example, according to the reconstruction quality, according to the bitrate, or according to a combination of these aspects. Determining which neural network performs the best may include comparing the network overfitted from a combination of networks overfitted on neighboring blocks to the network overfitted from the baseline network Fig. 4 shows an alternative strategy for the first embodiment, wherein the evaluation is performed by running candidate starting neural networks on the current block 401 , and the best performing neural network is then used as the starting neural network for being overfitted to the current block 401 . In this example, selecting the best neural network for the current block may include running (i.e. only inference stage, instead of overfitting) the candidate neural networks on the current block and of determining which neural network performs best without any optimization on the current block. The best neural network may be selected, and used as a reference encoder to overfit on the current block. The motivation is that if a neural network is already performing well on the current block 401 , it is likely to be close in weight space to the optimal point for the current block 401 . This strategy has the advantage of avoiding having multiple overfitting session for the current block 401 , as only inference phase is run on all candidate neural networks and then only one neural network is optimized. In a case, where the data type is video, a further additional neural network to be evaluated (either by running the overfitting process or by running the inference step) may be a neural network overfitted on the current block but on the previous frame, or on neighboring blocks on the previous frame, or a combination thereof. Furthermore, neural networks from multiple previous frames may be considered too.