Recently, the performance of neural image compression (NIC) has steadily improved thanks to the last line of study, reaching or outperforming state-of-the-art conventional codecs. Despite significant progress, current NIC methods still rely on ConvNet-based entropy coding, limited in modeling long-range dependencies due to their local connectivity and the increasing number of architectural biases and priors, resulting in complex underperforming models with high decoding latency. Motivated by the efficiency investigation of the Tranformer-based transform coding framework, namely SwinT-ChARM, we propose to enhance the latter, as first, with a more straightforward yet effective Tranformer-based channel-wise auto-regressive prior model, resulting in an absolute image compression transformer (ICT). Through the proposed ICT, we can capture both global and local contexts from the latent representations and better parameterize the distribution of the quantized latents. Further, we leverage a learnable scaling module with a sandwich ConvNeXt-based pre-/post-processor to accurately extract more compact latent codes while reconstructing higher-quality images. Extensive experimental results on benchmark datasets showed that the proposed framework significantly improves the trade-off between coding efficiency and decoder complexity over the versatile video coding (VVC) reference encoder (VTM-18.0) and the neural codec SwinT-ChARM. Moreover, we provide model scaling studies to verify the computational efficiency of our approach and conduct several objective and subjective analyses to bring to the fore the performance gap between the adaptive image compression transformer (AICT) and the neural codec SwinT-ChARM.

翻译：近年来，得益于持续的研究进展，神经图像压缩（NIC）的性能稳步提升，已达到或超越最先进的传统编解码器。尽管取得了显著进步，当前NIC方法仍依赖基于卷积神经网络（ConvNet）的熵编码，由于其局部连接特性以及不断增加的架构偏差和先验条件，其在长距离依赖建模方面存在局限，导致模型复杂度高、解码延迟大且性能欠佳。受基于Transformer的变换编码框架（即SwinT-ChARM）效率研究的启发，我们首先提出一种更简洁而有效的基于Transformer的通道自回归先验模型以增强该框架，由此产生绝对图像压缩变换器（ICT）。通过所提出的ICT，我们能够从潜在表示中捕获全局和局部上下文，并更优地参数化量化潜在变量的分布。此外，我们利用可学习缩放模块与夹层ConvNeXt架构的前/后处理器，在重建更高质量图像的同时精确提取更紧凑的潜在编码。在基准数据集上的大量实验表明，与通用视频编码（VVC）参考编码器（VTM-18.0）及神经编解码器SwinT-ChARM相比，所提框架显著改善了编码效率与解码复杂度之间的权衡。进一步，我们通过模型缩放研究验证了方法的计算效率，并开展了多项客观与主观分析，以凸显自适应图像压缩变换器（AICT）与神经编解码器SwinT-ChARM之间的性能差异。