Fast Implicit Neural Representation Image Codec in Resource-limited
  Devices
Xiang Liu Jiahong Chen Bin Chen Zimo Liu Baoyi An Shu-Tao Xia
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 outperform popular AE-based codecs in
constrained environments in terms of both quality and decoding time, or achieve
state-of-the-art reconstruction quality compared to other INR codecs.