*4.2. Key Metrics*

Between different compression algorithms, the compared key factors are the processing duration and the errors in combination with compression ratios to achieve an accurate reconstruction result. To ensure a valid comparison of the different types of compression algorithms, different key metrics are used. The compression ratio CR is defined in (8).

$$CR = \frac{\text{size of the input dataset (measured, unccompressed)}}{\text{size of the output dataset (compressed)}} \tag{8}$$

Following this definition, values greater than 1 indicate compression and values less than 1 imply expansion. The loss of information will be measured by comparing the reconstructed data matrix **XR** (with their rows and columns *n*row · *n*col) with the original data matrix **X**. The so-called MAE—mean absolute error is defined in (9).

$$MAE = \frac{1}{n\_{\text{row}} \cdot n\_{\text{col}}} \sum\_{i=1}^{n\_{\text{row}}} \sum\_{j=1}^{n\_{\text{col}}} |\mathbf{X}(i,j) - \mathbf{X}\mathbf{R}(i,j)|\tag{9}$$
