Lossy compression solutions have grown up during the last decades because of the increment of the data rate in the new-generation hyperspectral sensors, however linear compression techniques include useless information on regions of little interest for the final application and at the same time scarce information on areas of interest. In this paper, a transform-based lossy compressor, HyperLCA, has been extended to include a run-time adaptive distortion feature that brings multiple compression ratios in a same scenario. The solution has been designed to keep the same hardware-friendly feature, just like its previous version, specifically conceived to ease the deployment of the solution on reconfigurable hardware devices (FPGAs). The experiments demonstrate that the new version of the compressor is able to process 1024x1024 hyperspectral images and 180 spectral bands (377.5MB) in 0.935 seconds with a power consumption of 1.145 watts. In addition, experimental results also reveal that our architecture features high throughput (MSamples/s) and remarkable energy-efficiency (MB/s per watt) trade-offs, 10x and 6x greater than the best state-of-the-art solution, respectively.