An overview of bit-depth enhancement: Algorithm datasets and evaluation

Abstract

To enhance the visualization of images on high dynamic range (HDR) monitors, it is essential to employ bit-depth enhancement (BDE) methods for converting low bit-depth contents into high bit-depth contents. While many methods have been proposed in recent years, to the best of our knowledge, there is no benchmark to analyze the state-of-the-art methods thoroughly. In this paper, we provide a detailed review of current bit-depth enhancement algorithms, and categorized them into four types: classic pixel-independent methods, traditional spatial context-aware methods, deep learning based spatial BDE methods and fusion based spatio-temporal BDE methods. Meanwhile, we have conducted extensive and fair experimental comparisons to evaluate the effectiveness of each algorithm. Two typical evaluation metrics PSNR and SSIM are employed, and accordingly, we provide a thorough analysis and guidance for future work. This benchmark for bit-depth enhancement aims to benefit related researches in image restoration. The relevant codes and datasets are available at https://github.com/TJUMMG/BDE.