Improved Positional Encoding for Implicit Neural Representation based
  Compact Data Representation
Bharath Bhushan Damodaran Francois Schnitzler Anne Lambert Pierre Hellier
Positional encodings are employed to capture the high frequency information
of the encoded signals in implicit neural representation (INR). In this paper,
we propose a novel positional encoding method which improves the reconstruction
quality of the INR. The proposed embedding method is more advantageous for the
compact data representation because it has a greater number of frequency basis
than the existing methods. Our experiments shows that the proposed method
achieves significant gain in the rate-distortion performance without
introducing any additional complexity in the compression task and higher
reconstruction quality in novel view synthesis.