Research Information System
TRAITEMENT DU SIGNAL, vol.43, no.2, pp.675-685, 2026 (Peer-Reviewed Journal)
The vast amount of data generated by multispectral (MS) satellites makes image compression critical for remote sensing applications, as storing, processing, and downlinking these volumes to ground stations poses significant challenges. Temporal correlations in multitemporal images captured on different dates of the same scene can improve compression efficiency. This study builds on MultiTempGAN, a generative adversarial network (GAN) that predicts target MS images from reference MS images and improves model efficiency by applying 6-bit post-training non-uniform quantization (NUQ) to the generator without retraining, via piecewise linear quantization (PLQ). Experiments on Sentinel-2 MS image pairs reveal that 6-bit PLQ improves Laplacian mean square error (LMSE), signal-to-noise ratio (SNR), and bits-per-pixel (bpp) metrics compared to those of Q-MultiTempGAN, the uniformly quantized version of MultiTempGAN. Relative to full-precision MultiTempGAN, it incurs only a 0.46% decrease in SNR and a 33.33% increase in LMSE, yet achieves an 81.18% reduction in bpp. These results demonstrate that 6-bit PLQ yields a significantly smaller model with minimal loss in reconstruction accuracy, supporting more practical deployment of temporal-prediction-based MS compression.