Improved Positional Encoding for Implicit Neural Representation based Compact Data Representation

Bharath Bhushan Damodaran, François Schnitzler, Anne Lambert, Pierre Hellier

Abstract: 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.