Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner, resulting in two problems when deployed for practical applications. First, parallel acceleration of the autoregressive entropy model cannot be achieved due to serial decoding. Second, full-resolution inference often causes the out-of-memory(OOM) problem with limited GPU resources, especially for high-resolution images. Block partition is a good design choice to handle the above issues, but it brings about new challenges in reducing the redundancy between blocks and eliminating block effects. To tackle the above challenges, this paper provides a learned block-based hybrid image compression (LBHIC) framework. Specifically, we introduce explicit intra prediction into a learned image compression framework to utilize the relation among adjacent blocks. Superior to context modeling by linear weighting of neighbor pixels in traditional codecs, we propose a contextual prediction module (CPM) to better capture long-range correlations by utilizing the strip pooling to extract the most relevant information in neighboring latent space, thus achieving effective information prediction. Moreover, to alleviate blocking artifacts, we further propose a boundary-aware postprocessing module (BPM) with the edge importance taken into account. Extensive experiments demonstrate that the proposed LBHIC codec outperforms the VVC, with a bit-rate conservation of 4.1%, and reduces the decoding time by approximately 86.7% compared with that of state-of-the-art learned image compression methods.
翻译:最近关于学习的图像压缩的著作以完整分辨率的方式进行编码和解码过程,从而在实际应用部署时造成两个问题。首先,由于序列解码,自动递减的映射模型无法实现同步加速。第二,完全解析的推论往往导致使用有限GPU资源,特别是高分辨率图像的模拟(OOM)问题。块分割是处理上述问题的一个很好的设计选择,但在减少区块之间的冗余和消除区块效应方面带来了新的挑战。为了应对上述挑战,本文件提供了一个基于块块的混合图像压缩(LBHIC)框架。具体地说,我们将明确的内部预测引入一个学习的图像压缩框架,以便利用相邻区块之间的关系。通过对传统代码中邻居像素进行线性加权,我们提议一个背景预测模块(CPM),以便通过利用条形集合来获取相邻空间中最相关的信息,从而实现有效的信息预测。此外,为了减轻基于屏蔽的图像压缩缩缩(LC)的图像压缩框架,我们进一步提议将一个比重的图像缩缩缩缩缩缩缩缩缩缩缩缩图表(B)的缩缩缩缩缩缩缩缩图表,以显示BBBBBBBBBB的缩略图的缩略图。