Power Cost Comparison of Neural-Network Compression Methods for Satellite Imagery

In recent years, neural networks have been a key advance in the field of image compression. However, although their performance is highly competitive with established conventional methods, their high computational cost limits their use in settings where available power is a significant limitation, such as satellites and other remote sensing platforms. This problem has been addressed by recent contributions that have proposed reduced-complexity neural image codecs based on compressive autoencoders with hyperprior. These codecs allow for joint compression and denoising, variable-rate or fixed-quality compression. This paper proposes a comparison of the theoretical complexity of several neural codecs, as well as their encoding runtime and power consumption on low-power devices. This study includes comparisons between fixed-rate, precise rate allocation, and fixed-quality compression modes. The viability of their deployment on remote sensing missions is discussed based on these results.