  Neural fields, also known as implicit neural representations (INRs), have
shown a remarkable capability of representing, generating, and manipulating
various data types, allowing for continuous data reconstruction at a low memory
footprint. Though promising, INRs applied to video compression still need to
improve their rate-distortion performance by a large margin, and require a huge
number of parameters and long training iterations to capture high-frequency
details, limiting their wider applicability. Resolving this problem remains a
quite challenging task, which would make INRs more accessible in compression
tasks. We take a step towards resolving these shortcomings by introducing
neural representations for videos NeRV++, an enhanced implicit neural video
representation, as more straightforward yet effective enhancement over the
original NeRV decoder architecture, featuring separable conv2d residual blocks
(SCRBs) that sandwiches the upsampling block (UB), and a bilinear interpolation
skip layer for improved feature representation. NeRV++ allows videos to be
directly represented as a function approximated by a neural network, and
significantly enhance the representation capacity beyond current INR-based
video codecs. We evaluate our method on UVG, MCL JVC, and Bunny datasets,
achieving competitive results for video compression with INRs. This achievement
narrows the gap to autoencoder-based video coding, marking a significant stride
in INR-based video compression research.
