  Over the last few years, neural image compression has gained wide attention
from research and industry, yielding promising end-to-end deep neural codecs
outperforming their conventional counterparts in rate-distortion performance.
Despite significant advancement, current methods, including attention-based
transform coding, still need to be improved in reducing the coding rate while
preserving the reconstruction fidelity, especially in non-homogeneous textured
image areas. Those models also require more parameters and a higher decoding
time. To tackle the above challenges, we propose ConvNeXt-ChARM, an efficient
ConvNeXt-based transform coding framework, paired with a compute-efficient
channel-wise auto-regressive prior to capturing both global and local contexts
from the hyper and quantized latent representations. The proposed architecture
can be optimized end-to-end to fully exploit the context information and
extract compact latent representation while reconstructing higher-quality
images. Experimental results on four widely-used datasets showed that
ConvNeXt-ChARM brings consistent and significant BD-rate (PSNR) reductions
estimated on average to 5.24% and 1.22% over the versatile video coding (VVC)
reference encoder (VTM-18.0) and the state-of-the-art learned image compression
method SwinT-ChARM, respectively. 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 next generation ConvNet, namely ConvNeXt, and Swin Transformer.
