Deep image compression performs better than conventional codecs, such as JPEG, on natural images. However, deep image compression is learning-based and encounters a problem: the compression performance deteriorates significantly for out-of-domain images. In this study, we highlight this problem and address a novel task: universal deep image compression. This task aims to compress images belonging to arbitrary domains, such as natural images, line drawings, and comics. To address this problem, we propose a content-adaptive optimization framework; this framework uses a pre-trained compression model and adapts the model to a target image during compression. Adapters are inserted into the decoder of the model. For each input image, our framework optimizes the latent representation extracted by the encoder and the adapter parameters in terms of rate-distortion. The adapter parameters are additionally transmitted per image. For the experiments, a benchmark dataset containing uncompressed images of four domains (natural images, line drawings, comics, and vector arts) is constructed and the proposed universal deep compression is evaluated. Finally, the proposed model is compared with non-adaptive and existing adaptive compression models. The comparison reveals that the proposed model outperforms these. The code and dataset are publicly available at https://github.com/kktsubota/universal-dic.
翻译:深度图像压缩比自然图像的常规调制解码器(如 JPEG) 表现更好。 但是, 深度图像压缩是基于学习的, 遇到一个问题: 外部图像的压缩性能明显恶化。 在此研究中, 我们突出这一问题并处理一项新颖的任务: 普遍的深度图像压缩。 此项任务旨在压缩属于自然图像、 线图和漫画等任意域的图像。 为了解决这个问题, 我们提议了一个内容调整优化框架; 这个框架使用一个预培训的压缩模型, 并在压缩过程中将模型调整为目标图像。 调制器被插入模型的解码器中。 对于每个输入图像, 我们的框架优化了由编码器提取的潜值和调制参数, 包括率调制的调制参数。 调制参数是按每个图像额外传输的。 对于实验来说, 一个包含四个域( 自然图像、 线绘制、 漫画和矢量艺术) 的不受压缩的图像的基准数据集已经构建, 并且对拟议的通用深度压缩模型进行了评估。 最后, 提议的模型与非调制/ 现有调制模型进行了比较。 和现有的调制模型 。 正在将这些模型与现有的调制模型进行比较。 。 。 。 。 和现有的调制式 。 正在将这些模型 和现有的调制制式 。