CNN-BASED LOCAL TONE MAPPING IN THE PERCEPTUAL QUANTIZATION DOMAIN

A typical way of local tone mapping (TM) is based on multilayer decomposition of the source image. For this, the source image is decomposed into a base layer and a detail layer to compress from high dynamic range (HDR) to low dynamic range (LDR). Perceptual quantization (PQ) is a standardized non-linear transfer function for HDR content. It mimics the non-linearity of human vision by compressing more strongly in bright regions and less in dark areas, when converting luminance values to electrical signals. We propose a CNN-based pipeline for local TM, which operates on the low-frequency base layer of the luminance signal, while keeping the detail layer unchanged. The proposed method works entirely in the PQ domain and is adaptable to display peak luminance. The tone-mapped LDR images obtained with our learning-based approach show significant improvements in PSNR, while the network size is reduced compared to previous work. Our experiments on the HDR datasets from Fairchild and Funt show PSNR improvements of 8 dB compared to the state-of-the-art approaches.