Real-world applications of Visual Place Recognition (VPR) often rely on cloud computing, where query images or videos are transmitted elsewhere for visual localization. However, limited network bandwidth forces a reduction in image quality, which degrades global image descriptors and consequently VPR accuracy. We address this issue at the descriptor extraction level with a knowledge-distillation framework that transfers feature representations from high-quality images to low-quality ones, allowing VPR methods to produce more discriminative descriptors from the latter. Our approach leverages three complementary loss functions the Inter-channel Correlation Knowledge Distillation (ICKD) loss, Mean Squared Error (MSE) loss, and a weakly supervised Triplet loss to guide a student network in approximating the high-quality features produced by a teacher network. Extensive experiments on multiple VPR methods and datasets subjected to JPEG compression, resolution reduction, and video quantization demonstrate significant improvements in recall rates. Furthermore, our work fills a gap in the literature by exploring the impact of video-based data on VPR performance, thereby contributing to more reliable place recognition in resource-constrained environments.
