|
| Compression algorithm model | Compression algorithm type | Algorithm title | Advantages | Disadvantages |
|
| Single-algorithm compression model | Quantization-based compression algorithm | Scalar quantification [47] | Simple method and fast processing | Distorted data |
| Vector quantization [48, 49] | Better than scalar quantification | Limited distortion |
| Compression algorithm based on signal decomposition (threshold processing) | EMD [34] | Fast signal decomposition and fast compression | Easy to generate IMFs overlap, resulting in signal distortion |
| Intrinsic time-scale decomposition [38] | Better computational speed than EMD, high time–frequency resolution, the good compression effect | Due to linear interpolation, it is easy to distort the intersection position |
| VMD [50] | High decomposition accuracy, better decomposition of IMFs, accurate reduction of redundant information | With boundary effects, the parameters have a large impact on the results |
| LMD [51] | Better preservation of transient change information in the original signal | Still has endpoint effects and does not have fast algorithms |
| Compression algorithms based on sparse dictionary transformations (complete and overcomplete dictionaries) | STFT [52] | Capable of fast time and frequency conversion | Low resolution at high frequencies, resulting in signal loss during compression |
| DCT [53] | Fast processing time and good sparse transformation effect | The transformation method does not work with all signals |
| DWT [54] and LSWT [55] | With better time–frequency resolution, the LSWT algorithm does not consume memory | Excessive layer decomposition can result in wasted computational resources |
| Best orthogonal basis [56] | Able to obtain near-optimal signal representation | Sparse is less effective when the signal cannot be represented by orthogonal components |
| Orthogonal matching pursuit [57, 58] | Fast convergence to get a better sparse signal | The vertical projection of the processed signal is non-orthogonal and the number of iterations increases |
| Generalized morphological component analysis [59, 60] | Capable of adapting to different input signal types, improving calculation speed and signal separation accuracy | The parameters of the calculation need to be set in advance, and the set values of the parameters directly affect the results of the processing |
| Neural-network-based algorithm | RNN [43] | Very strong nonlinear mapping, high compression ratio (CR) | Requires training in the model and high computational resource requirements |
| Hybrid algorithm compression model | Lossy single algorithm + lossless compression single algorithm | Based on signal decomposition and Huffman [61] | Furthermore, increase in CR, no secondary data loss due to the introduction of lossless compression | This causes the complexity of the algorithm to increase and the data compression time to increase |
| | Based on sparse dictionary transform and RLE [62] | | |
|
