  Displaying high-quality images on edge devices, such as augmented reality
devices, is essential for enhancing the user experience. However, these devices
often face power consumption and computing resource limitations, making it
challenging to apply many deep learning-based image compression algorithms in
this field. Implicit Neural Representation (INR) for image compression is an
emerging technology that offers two key benefits compared to cutting-edge
autoencoder models: low computational complexity and parameter-free decoding.
It also outperforms many traditional and early neural compression methods in
terms of quality. In this study, we introduce a new Mixed AutoRegressive Model
(MARM) to significantly reduce the decoding time for the current INR codec,
along with a new synthesis network to enhance reconstruction quality. MARM
includes our proposed AutoRegressive Upsampler (ARU) blocks, which are highly
computationally efficient, and ARM from previous work to balance decoding time
and reconstruction quality. We also propose enhancing ARU's performance using a
checkerboard two-stage decoding strategy. Moreover, the ratio of different
modules can be adjusted to maintain a balance between quality and speed.
Comprehensive experiments demonstrate that our method significantly improves
computational efficiency while preserving image quality. With different
parameter settings, our method can achieve over a magnitude acceleration in
decoding time without industrial level optimization, or achieve
state-of-the-art reconstruction quality compared with other INR codecs. To the
best of our knowledge, our method is the first INR-based codec comparable with
Hyperprior in both decoding speed and quality while maintaining low complexity.
