Difference between compressive sensing and DCT-based compression? I am working on transmitting EEG signals over wireless body area network. I have applied two different compression techniques: DCT-based and compressive sensing (CS-based) approach. I noticed that the reconstructed signal is better using the DCT-based approach. Is this reasonable? I know that compression using DCT is lossy. The metrics used for evaluating the quality of the reconstructed signal is the normalized mean square error (NMSE) and the structural similarity index metric (SSIM).

Thanks in advance 



In case of not fulfilling the CS theoretical conditions, is it recommended to use DCT based compression for compressing biomedical signals to transmit them wirelessly over a body area network?
I am asking this question because most papers recommend the use of CS compression, due to the energy efficient consumption property
Comparing compressive sensing and traditional compression is a complicated task. Even  comparing two compression algorithms. 

One the one hand you have compression ratios, making sure the bits sent can be decompressed without other hidden bits. On the other hand, error measurements, rarely perfectly matched to the actual meaningful distorsions. [EDIT] Standard SSIM or NMSE are not well adapted to sparse signal.

Then, knowing the range over which one algorithm is better than the other is important, because one can be better at low-rates, the other at high-rates. There is rarely a free lunch in compression.

From a very mundane point of view, if you are in a compression range where the signal is not "sparse" enough (with respect to some basis) and the CS quantization is not really taken into account, yet sufficiently harmonic, it is well understandable that a DCT-like engineered algorithm can do better than a CS method if the CS theoretical conditions are not fulfilled.