  Neural fields have emerged as a powerful paradigm for representing various
signals, including videos. However, research on improving the parameter
efficiency of neural fields is still in its early stages. Even though neural
fields that map coordinates to colors can be used to encode video signals, this
scheme does not exploit the spatial and temporal redundancy of video signals.
Inspired by standard video compression algorithms, we propose a neural field
architecture for representing and compressing videos that deliberately removes
data redundancy through the use of motion information across video frames.
Maintaining motion information, which is typically smoother and less complex
than color signals, requires a far fewer number of parameters. Furthermore,
reusing color values through motion information further improves the network
parameter efficiency. In addition, we suggest using more than one reference
frame for video frame reconstruction and separate networks, one for optical
flows and the other for residuals. Experimental results have shown that the
proposed method outperforms the baseline methods by a significant margin. The
code is available in https://github.com/daniel03c1/eff_video_representation
