Gain-MLP: Improving HDR Gain Map Encoding via a Lightweight MLP

While most images shared on the web and social media platforms are encoded in standard dynamic range (SDR), many displays now can accommodate high dynamic range (HDR) content. Additionally, modern cameras can capture images in an HDR format but convert them to SDR to ensure maximum compatibility with existing workflows and legacy displays. To support both SDR and HDR, new encoding formats are emerging that store additional metadata in SDR images in the form of a gain map. When applied to the SDR image, the gain map recovers the HDR version of the image as needed. These gain maps, however, are typically down-sampled and encoded using standard image compression, such as JPEG and HEIC, which can result in unwanted artifacts. In this paper, we propose to use a lightweight multi-layer perceptron (MLP) network to encode the gain map. The MLP is optimized using the SDR image information as input and provides superior performance in terms of HDR reconstruction. Moreover, the MLP-based approach uses a fixed memory footprint (10 KB) and requires no additional adjustments to accommodate different image sizes or encoding parameters. We conduct extensive experiments on various MLP based HDR embedding strategies and demonstrate that our approach outperforms the current state-of-the-art.

Using Decoupled Features for Photo-realistic Style Transfer

In this work we propose a photorealistic style transfer method for image and video that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of sample statistics. The novel aspects of our approach include matching decoupled moments of higher order than in common style transfer approaches, and matching a descriptor of the power spectrum so as to characterize and transfer diffusion effects between source and target, which is something that has not been considered before in the literature. The results are of high visual quality, without spatio-temporal artifacts, and validation tests in the form of observer preference experiments show that our method compares very well with the state-of-the-art. The computational complexity of the algorithm is low, and we propose a numerical implementation that is amenable for real-time video application. Finally, another contribution of our work is to point out that current deep learning approaches for photorealistic style transfer don't really achieve photorealistic quality outside of limited examples, because the results too often show unacceptable visual artifacts.