Recently deep learning-based methods have been applied in image compression and achieved many promising results. In this paper, we propose an improved hybrid layered image compression framework by combining deep learning and the traditional image codecs. At the encoder, we first use a convolutional neural network (CNN) to obtain a compact representation of the input image, which is losslessly encoded by the FLIF codec as the base layer of the bit stream. A coarse reconstruction of the input is obtained by another CNN from the reconstructed compact representation. The residual between the input and the coarse reconstruction is then obtained and encoded by the H.265/HEVC-based BPG codec as the enhancement layer of the bit stream. Experimental results using the Kodak and Tecnick datasets show that the proposed scheme outperforms the state-of-the-art deep learning-based layered coding scheme and traditional codecs including BPG in both PSNR and MS-SSIM metrics across a wide range of bit rates, when the images are coded in the RGB444 domain.
翻译:最近深层次的学习方法应用于图像压缩,并取得了许多有希望的成果。 在本文中,我们提出一个经过改进的混合层图像压缩框架,将深层学习和传统图像编码器结合起来。在编码器中,我们首先使用一个神经神经元网络(CNN)来获取输入图像的缩略图,这种图像由FLIF编码器作为位流的底层,无损地被FLIF编码器编码。另一个CNN从重建的缩写式中获得了对输入的粗略重建。输入和粗皮重建之间的剩余部分随后由基于H.265/HEVC的BPG编码器作为位流的增强层进行编码。使用Kodak和Tecnick数据集的实验结果显示,拟议的方案超越了基于深层学习的层代码系统以及包括PPG在内的传统编码系统,在PSNR和MS-SSIM指标中,在广泛的位数上,当图像在RGB444域进行编码时,这些图象被编码时,其结果就超越了。