Automated Detection of Colorspace Via Convolutional Neural Network

Abstract

Prior to the advent of ITU-R Recommendation BT.709 the overwhelming majority of compressed digital video and imagery used the colorspace conversion matrix specified in ITU-R Recommendation BT.601. The introduction of high-definition video formats led to the adoption of Rec. BT.709 for use in colorspace conversion by new systems, and this resulted in confusion in the industry. Specifically, video decoders may not be able to determine the correct matrix to use for converting from the luma/chroma representation used for coding, to the Red-Green-Blue representation needed for display. This confusion has led to a situation where some viewers of decompressed video streams experience subtle, but noticeable, errors in coloration. We have successfully developed and trained a deep convolutional neural network to address this heretofore unsolved problem. We obtained outstanding accuracy on ImageNet data, and on YouTube video frames, and our work can be expected to lead to more accurate color rendering delivered to users of digital imaging and video systems.