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：近期，得益于最新研究，神经图像压缩（NIC）的性能稳步提升，已接近或超越传统最先进编解码器。尽管取得了显著进展，当前NIC方法仍依赖基于卷积网络的熵编码，其局部连接性及日益增多的架构偏置与先验限制了长程依赖建模能力，导致模型复杂、性能欠佳且解码延迟高。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且高效的基于Transformer的通道自回归先验模型以增强原框架，从而构建绝对图像压缩Transformer（ICT）。通过所提出的ICT，我们能够从潜在表征中捕获全局与局部上下文信息，并更优地参数化量化潜在变量的分布。此外，我们利用含三明治式ConvNeXt前/后处理器的可学习缩放模块，在重构更高质量图像的同时精准提取更紧凑的潜在编码。在基准数据集上的大量实验表明，所提框架显著提升了编码效率与解码器复杂度之间的权衡，优于通用视频编码（VVC）参考编码器（VTM-18.0）及神经编解码器SwinT-ChARM。进一步地，我们通过模型缩放研究验证了方法的计算效率，并进行了多项客观与主观分析，以凸显自适应图像压缩Transformer（AICT）与神经编解码器SwinT-ChARM之间的性能差距。