  Learned Image Compression (LIC) gradually became more and more famous in
these years. The hyperprior-module-based LIC models have achieved remarkable
rate-distortion performance. However, the memory cost of these LIC models is
too large to actually apply them to various devices, especially to portable or
edge devices. The parameter scale is directly linked with memory cost. In our
research, we found the hyperprior module is not only highly over-parameterized,
but also its latent representation contains redundant information. Therefore,
we propose a novel pruning method named ERHP in this paper to efficiently
reduce the memory cost of hyperprior module, while improving the network
performance. The experiments show our method is effective, reducing at least
22.6% parameters in the whole model while achieving better rate-distortion
performance.
