面向学习型图像压缩的因果上下文调整损失
Causal Context Adjustment Loss for Learned Image Compression
AI总结：
针对现有学习型图像压缩依赖人工因果上下文、Transformer类方案推理负担重的问题，提出因果上下文调整损失引导生成更优上下文，并搭配非均匀通道分组CNN模型，实现推理延迟与率失真性能的良好平衡。
摘要：
近年来，学习型图像压缩（LIC）技术在率失真（RD）性能方面已显著超越传统方法。目前多数学习型技术均基于VAE（变分自编码器）并搭配自回归熵模型，通过利用解码后的因果上下文显著提升了RD性能。但现有方法高度依赖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。本文首次研究了显式调整因果上下文的方法，提出了因果上下文调整损失（CCA-loss）。通过施加CCA-loss，我们让神经网络能够自发地将重要信息调整到自回归熵模型的早期阶段。此外，随着transformer技术的显著发展，其变体已被许多最先进（SOTA）的LIC技术采用。但现有计算设备未能很好地适配注意力机制的计算，导致计算量和推理延迟负担过重。为解决该问题，我们构建了一个卷积神经网络（CNN）图像压缩模型，并采用非均匀通道分组策略以实现高效率。最终，经CCA-loss训练的基于CNN的LIC网络在推理延迟和率失真性能之间取得了极佳的平衡。
英文摘要：
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.
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