In this paper, we present PCT-Net, a simple and general image harmonization method that can be easily applied to images at full resolution. The key idea is to learn a parameter network that uses downsampled input images to predict the parameters for pixel-wise color transforms (PCTs) which are applied to each pixel in the full-resolution image. We show that affine color transforms are both efficient and effective, resulting in state-of-the-art harmonization results. Moreover, we explore both CNNs and Transformers as the parameter network and show that Transformers lead to better results. We evaluate the proposed method on the public full-resolution iHarmony4 dataset, which is comprised of four datasets, and show a reduction of the foreground MSE (fMSE) and MSE values by more than 20% and an increase of the PSNR value by 1.4dB while keeping the architecture light-weight. In a user study with 20 people, we show that the method achieves a higher B-T score than two other recent methods.