Learning-based Framework for Color Conversion between Digital Cinema Package and Streaming Movie

Abstract

Humans feel different experiences when viewing a cinema at the theater and home. This paper presents methods to reduce the difference in the viewing experience. Based on the workflow of digital cinema distribution, the proposed method attempts to minimize the color difference between digital cinema packages (DCP) for the theater and streaming movies. For end-to-end mapping between the DCP and streaming movie, this paper proposes a convolutional neural network (CNN)-based color conversion algorithm based on the SMPTE standard. The proposed method consists of three steps: i) color conversion using standard matrices, ii) color conversion using the CNN, and iii) color saturation error removal by fusing the results in steps i) and ii). The proposed method enhances the color of TV streaming images because it minimizes the color difference from the DCP and appropriately extends the color gamut. As a result, the proposed method can provide consumers with indistinguishable quality from a DCP movie at the theater.