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，得益于最新研究方向，神经图像压缩（NIC）的性能稳步提升，已达到或超越传统顶级编解码器。尽管取得显著进展，当前NIC方法仍依赖基于卷积神经网络的熵编码，因其局部连接性和日益增加的架构偏置与先验，限制了长程依赖建模能力，导致模型复杂、性能不足且解码延迟较高。受基于Transformer的变换编码框架SwinT-ChARM效率研究的启发，我们首先通过一种更简洁有效的基于Transformer的通道自回归先验模型对其加以改进，从而提出绝对图像压缩Transformer（ICT）。通过所提出的ICT，我们能够从潜在表示中捕获全局与局部上下文，并更优地参数化量化潜在变量的分布。此外，我们利用一种带夹层ConvNeXt预处理器/后处理器的可学习缩放模块，在重建更高质量图像的同时精确提取更紧凑的潜在编码。基准数据集上的广泛实验表明，所提框架在编码效率与解码复杂度之间的权衡上显著优于通用视频编码（VVC）参考编码器（VTM-18.0）和神经编解码器SwinT-ChARM。此外，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。