Convolutional Neural Networks in the Color Space Transform Problem

Key Points

The new method achieves a 39% reduction in CIEDE2000 color difference, enhancing image quality.
It also improves the angular distance in the ProLab space by 35%, demonstrating superior performance.
The method utilizes a convolutional neural network to consider scene semantics in color space transformations.
This advancement highlights the importance of contextual information over traditional pointwise image transformations.

Abstract

The problem on color space transform as a stage of the digital image formation pipeline is investigated. The available methods for solving this problem mainly perform the pointwise image transformation without considering the contextual information. The proposed new color space transform method based on the LW-ISP convolutional neural network can take into account the scene semantics. The proposed method demonstrates significant advantages over the modern color transform approaches, specifically, a 39% reduction of the CIEDE2000 color difference and a 35% reduction of the angular distance in the ProLab space.

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Convolutional Neural Networks in the Color Space Transform Problem

Key Points

Abstract

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