Efficient-HDRTV: Efficient SDR to HDR Conversion for HDR TV

The existing deep neural network (DNN) based SDR (Standard dynamic range) to HDR (High dynamic range) conversion methods outperform conventional methods, but they are either too large to implement on a device or with quantization artifacts generated on smooth regions on an image. We propose an efficient neural network for the SDR to HDR conversion, namely "Efficient-HDRTV". It consists of two efficient structures GIM (Global Inverse Mapping) and LIM (Local Inverse Mapping). The key features of GIM and LIM use the small series of a basis function with its coefficient function, which are implemented using small number of convolutions, logarithm and exponential functions. They are combined with other convolutional layers so that the entire network can be jointly trained for learning inverse tone, enhanced details and expanded color gamut from SDR to HDR. Thanks to the GIM and LIM, we can keep our network small with good performance. Our experimental results show that Efficient-HDRTV is much lighter but performs better than the state of the arts.