A correlation exists between luminance samples and chrominance samples of a color image. It is beneficial to exploit such interchannel redundancy for color image compression. We propose an algorithm that predicts chrominance components Cb and Cr from the luminance component Y. The prediction model is trained by supervised learning with Laplacian‐regularized least squares to minimize the total prediction error. Kernel principal component analysis mapping, which reduces computational complexity, is implemented on the same point set at both the encoder and decoder to ensure that predictions are identical at both the ends without signaling extra location information. In addition, chrominance subsampling and entropy coding for model parameters are adopted to further reduce the bit rate. Finally, luminance information and model parameters are stored for image reconstruction. Experimental results show the performance superiority of the proposed algorithm over its predecessor and JPEG, and even over JPEG‐XR. The compensation version with the chrominance difference of the proposed algorithm performs close to and even better than JPEG2000 in some cases.

中文翻译：

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基于监督学习的彩色图像压缩算法

彩色图像的亮度样本和色度样本之间存在相关性。利用这种通道间冗余来进行彩色图像压缩是有益的。我们提出了一种从亮度分量Y预测色度分量Cb和Cr的算法。该预测模型是通过监督学习与拉普拉斯正则化最小二乘法进行训练的，以最大程度地减少总的预测误差。减少编码复杂度的内核主成分分析映射是在编码器和解码器上设置的同一点上实现的，以确保两端的预测是相同的，而不会用信号发送额外的位置信息。另外，采用色度二次采样和模型参数熵编码来进一步降低比特率。最后，存储亮度信息和模型参数以进行图像重建。实验结果表明，该算法在性能上优于其前身和JPEG，甚至优于JPEG-XR。在某些情况下，所提出算法具有色差的补偿版本的性能接近甚至优于JPEG2000。