用卷积网络优化彩色网格，压缩图像颜色数据。
Convolutional Deep Colorization for Image Compression: A Color Grid Based Approach
- 基于彩色网格设计卷积神经网络，自动保留颜色信息。
- 实现高压缩比，复原图像仍保持高保真度。
- 适合图像压缩与低带宽传输场景使用。
图像压缩优化技术持续受到学术界与产业界的关注。其中，图像着色方法因其能减少图像所需存储的颜色数据量而展现出潜力。本文聚焦于优化基于彩色网格的全自动图像颜色信息保留方法，针对卷积着色网络架构进行改进，旨在最小化存储颜色信息的同时，仍能准确还原图像色彩。实验结果表明，该方法在实现较高图像压缩比的同时，仍可成功复现图像色彩，达到较高的CSIM值，具备实际应用前景。
原文摘要 · Abstract (English)
The search for image compression optimization techniques is a topic of constant interest both in and out of academic circles. One method that shows promise toward future improvements in this field is image colorization since image colorization algorithms can reduce the amount of color data that needs to be stored for an image. Our work focuses on optimizing a color grid based approach to fully-automated image color information retention with regard to convolutional colorization network architecture for the purposes of image compression. More generally, using a convolutional neural network for image re-colorization, we want to minimize the amount of color information that is stored while still being able to faithfully re-color images. Our results yielded a promising image compression ratio, while still allowing for successful image recolorization reaching high CSIM values.
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