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Alexey Timin for PANDA

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Wavelet Buffer In A Nutshell

Wavelet Buffer is a core technology for data processing in the PANDA|Drift Platform. It provides the following

  • Storing data as wavelet subbands so that you can restore the smallest version of the signal
  • Denoising high-frequency spectrum
  • Compressing data with float-compression, so you can choose the proper “float” for your data with adjustable size of the mantissa.

The Simplest Explanation of Wavelet Decomposition

There is a beautiful and tricky math behind the wavelet decomposition, but for our engineering tasks it is enough to
consider the wavelet decomposition as a cascade of low-pass and high-pass filters + N/2 resampler. When we pass our
signal through the low-pass filter, we have twice smaller version of our signal (approximation or low frequency subband)
. When we pass
our signal through the high-pass filter, we have twice smaller subband with details and noise (detalization or
high-frequency subband).

Wavelet Decomposition

So in the end we have two subbands of N/2 size that can be composed again, and we can have the original signal. This
approach has the following advantages:

  1. We have noise in the high-frequency subband separately of the low frequency signal, and we can denoise it by setting the small values to zero and make the subband sparse. This is a critical part because when you measure some vibration of some machine that is currently stopped, you have only noise instead of the signal. So, we can reduce the size of this data almost to zero and store only the real signal on the disk.
  2. We have a smaller version of the signal in low-level subband. For example, if we would like to show our signal in the browser and the monitor has resolution 1920x1200, it doesn’t make any sense to provide the signal with the size more than 1920 points. So, we can compose only some low-level subbands without reconstructing the whole original signal.

In the diagram, I showed only one step of the decomposition, but we can decompose the signal recursively, by applying
the wavelet decomposition on the low-frequency subband again. Therefore, we can decompose our signal to a few subbands:
N/1 H-freq subband (D), N/2 H-freq subband (AD), …. N/M L-freq subband (ADm), where N size of the original signal and M
– the number of the decomposition steps. After the decomposition, we have a representation of our signal as an array of
subbands for 3 steps it will be [AAAA, ADDD, ADD, AD, D]. When we make many steps of the decomposition, we do better
denoising and zooming because now we can get signal of sizes N/2, N/4 … or N/M if we restore only part of all the steps.

Wavelet Decomposition can be applied to 2-D matrices, the difference is that we have 4 types of subbands:

  • Low frequency approximation (A)
  • High frequency detalization by vertical (Dv)
  • High frequency detalization by horizontal (Dh)
  • High frequency detalization by diagonal (Dd)

Here you can see how it looks like for one decomposition step:

Wavelet decomposition for an image


In a nutshell, Wavelet is just a wrapper around the wavelet subbands:

Structure of Wavelet Buffer

It has 4 main methods:

  • Decompose. It decomposes the input signal to the subbands and denoises the high-frequency ones.
  • Compose. It reconstructs the original signal from subbands. It may reconstruct only few steps of the decomposition so that we can get approximation of the signal N/2, N/4 or N/M (N size of original signal, M—number decomposition steps) and spare some computation time.
  • Serialize. However, subbands are sparse after denoising, they still contain zeros in the memory and occupy the same memory space as before denoising. This method compresses the subbands to binary data with Sparse Float Compression algorithm. We use it before saving a Wavelet Buffer onto the disk or sending it through network. You can read about it below.
  • Parse. Decompresses a Wavelet Buffer and restores subbands

Sparse Float Compression

Sparse Float Compression Algorithm is a specific compression algorithm to compress sparse float data. It does three

  1. Removing zeros and add leave only values.
  2. Compressing the data, so that the more frequent values spend fewer bits for encoding.
  3. Casts the values of the subbands to an adjustable float container, so that we can choose the size of float mantissa. We need it if we have lower resolution of our data, and 32-bit float is too big for this.

The current implementation of the compressor supports the following size of the mantissa:

Size in bits Compression level in WaveletBuffer::Serialize
No compression. The algorithm is bypassed. 0
23 (float32) 1
21 2
20 3
... ...
7 (bfloat) 16

!!! warning
Because we use float values, we always have some error, which depends on the range of the values. You should adjust
compression level by using knowledge about your data.

Scalar Values

Some data don’t need any denoising and compression, for example GPS coordinates or slow data from automation systems. In
this case, we can switch off the compression and wavelet composition in WB and make it simple and stupid a vector or
matrix of the data.

Wavelet Functions

WaveletBuffer supports a few wavelet functions to tweak the wavelet decomposition:

Name Description
NONE No wavelet decomposition and denoising. See Scalar Values for detail.
DB1 Daubechies wavelet DB2 or Haar
... ..
DB5 Daubechies wavelet DB10

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