Understanding this Haar Wavelet Example
It looks like there is a problem with your scaling.  

The scaling for the DWT has the same interpretation as it does for the CWT.

For the DWT, the scale of the analyzing function (wavelet) is increased using a dyadic scale (increasing by factors of 2) in non-overlapping time intervals.  Increasing scale corresponds to narrower frequency distributions in the frequency domain.  Basically think of the analyzing function (wavelet) as a bandpass filter for which you are both narrowing the bandwidth and shifting down the center frequency by increasing the time scale.

As an example (see images below) I've transformed an audio signal using a CWT and then again using a DWT.  Notice that the results look similar, they scale in the same way, except in the DWT, the regions of analysis are discrete non-overlapping intervals (sharp boundaries).  Notice also that as the time scale increases (heading south on the CWT diagram), you have fewer DWT coefficients, so the number of blocks in the image decrease.  I don't see this kind of scaling in your data.