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，得益于最新的研究，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水平。尽管取得了显著进展，当前NIC方法仍依赖基于ConvNet的熵编码，因其局部连接性及不断增加的架构偏好与先验信息，导致模型在长距离依赖建模方面存在局限，从而形成性能欠佳且解码延迟高的复杂模型。受基于Transformer的变换编码框架（即SwinT-ChARM）的效率研究启发，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来增强该框架，从而构建出绝对图像压缩变换器（ICT）。通过所提出的ICT，我们能够从隐表示中捕捉全局与局部上下文信息，并更准确地参数化量化隐变量的分布。此外，我们利用一个可学习的缩放模块以及基于ConvNeXt的夹层式前后处理器，在重建更高质量图像的同时精确提取更紧凑的隐编码。在基准数据集上的大量实验表明，所提出的框架显著提升了编码效率与解码复杂度之间的权衡，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。同时，我们提供了模型缩放研究以验证方法的计算效率，并进行了多项客观与主观分析，凸显了自适应图像压缩变换器（AICT）与神经编解码器SwinT-ChARM之间的性能差距。