In recent years, learned image compression (LIC) technologies have surpassed conventional methods notably in terms of rate-distortion (RD) performance. Most present learned techniques are VAE-based with an autoregressive entropy model, which obviously promotes the RD performance by utilizing the decoded causal context. However, extant methods are highly dependent on the fixed hand-crafted causal context. The question of how to guide the auto-encoder to generate a more effective causal context benefit for the autoregressive entropy models is worth exploring. In this paper, we make the first attempt in investigating the way to explicitly adjust the causal context with our proposed Causal Context Adjustment loss (CCA-loss). By imposing the CCA-loss, we enable the neural network to spontaneously adjust important information into the early stage of the autoregressive entropy model. Furthermore, as transformer technology develops remarkably, variants of which have been adopted by many state-of-the-art (SOTA) LIC techniques. The existing computing devices have not adapted the calculation of the attention mechanism well, which leads to a burden on computation quantity and inference latency. To overcome it, we establish a convolutional neural network (CNN) image compression model and adopt the unevenly channel-wise grouped strategy for high efficiency. Ultimately, the proposed CNN-based LIC network trained with our Causal Context Adjustment loss attains a great trade-off between inference latency and rate-distortion performance.

翻译：近年来，学习式图像压缩技术在率失真性能方面已显著超越传统方法。当前多数学习式技术采用基于变分自编码器的自回归熵模型，其通过利用解码后的因果上下文明显提升了率失真性能。然而，现有方法高度依赖于固定的人工设计因果上下文。如何引导自编码器生成对自回归熵模型更有效的因果上下文，是值得探索的问题。本文首次尝试通过提出的因果上下文调整损失来显式调整因果上下文。通过施加CCA损失，我们使神经网络能够将重要信息自发调整至自回归熵模型的早期阶段。此外，随着Transformer技术的显著发展，其变体已被许多先进的学习式图像压缩技术采用。现有计算设备尚未良好适配注意力机制的计算，这导致计算量与推理延迟的负担。为克服此问题，我们建立了基于卷积神经网络的图像压缩模型，并采用非均匀通道分组策略以提升效率。最终，采用因果上下文调整损失训练的基于CNN的学习式图像压缩网络，在推理延迟与率失真性能之间实现了优异的平衡。