Evaluation of lossy compression algorithms using discrete cosine transform for sounding rocket vibration data

Abstract

This work evaluates the performance of compression algorithms based on Discrete Cosine Transform when used to encode sounding rockets' vibration signals. Different compression methods that use the three-step strategy (transform, quantization, and entropy encoder) are presented. The performances are evaluated using the relationship between the compression rate and distortion, the latter being measured using the Peak Signal-to-Noise Ratio (PSNR) and the difference observed in the Power Spectral Density (PSD). The focus of the work is on vibration signals from rocket launches conducted by the Institute of Aeronautics and Space of the Brazilian Air Force. The obtained results present a great variation for the vibration data in the test set. Compressors operating with PSNR close to 50 dB can achieve very high compression rates (over 100) for low power signals. However, if the vibration signal strength increases significantly, the compression rate can be much lower. For the worst case in the test set (signal with the highest power), the work shows that the best proposed method needs a rate of 9.8 to achieve a PSNR of 48.2 dB. When compared to the performance of recently published DCT-based compression algorithms, the proposed technique shows significant improvements. Given a PSNR around 50 dB, for signal with moderate power, improvements above 100% are observed in compression rate.

Microvibration streaming measurements using dynamic compressed sensing for satellites

“…The parameter variation δ − , δ + can be calculated by solving formula (14). The parameter ε continues to iterate until it equals 1.…”

“…In-orbit compression algorithms [13,14] provide a way to solve this problem. Redundant information in the sampled data can be discarded to reduce the amount of data.…”

Analyzing Data Reduction Techniques: An Experimental Perspective

“…Lossy data reduction techniques [10,[25][26][27]: these focus on reducing data by discarding some details considered less relevant to the analysis or less noticeable to human perception, and thus achieve higher compression ratios than other methods. Some examples of these techniques are transform encoding, Discrete Cosine Transform, Random Projection, Bzip2, or Fractal Compression [28][29][30]. Numerosity data reduction techniques [10,25,26] reduce the amount of data by capturing the overall trend or patterns of the data.…”