Image super-resolution (SR) plays an important role in video-based criminal investigation. SR algorithms based on convolutional neural networks are usually trained with the input of artificially synthesized low-resolution images to learn the mapping between high-resolution and low-resolution images, and thus they are difficult to be applied in the field of video-based criminal investigation. The degradation process of real low-resolution images is complex and unknown, and most of them are processed by compression algorithms, leading to false textures in SR images. Therefore, a new SR algorithm based on discrete cosine transform (DCT) and zero-shot learning is proposed for the video-based criminal investigation images in real scenarios. The algorithm takes advantage of the repetitive similarity within the images and utilizes sub-images from the input image for training. Different from the input of the previous SR network, the proposed algorithm takes the DCT coefficients of the sub-images as the input of the SR network to avoid magnifying compression artifacts of the input image and reduce false textures. The experimental results on the standard datasets and real criminal investigation images show that the proposed algorithm can reduce the false texture caused by compression artifacts.