Quality Map-guided Fidelity Compression Method for High-energy Regions of Spectral Data

LIU Xiangli; LI Zan; CHEN Yifeng; CHEN Le

doi:10.11999/JEIT250650

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LIU Xiangli, LI Zan, CHEN Yifeng, CHEN Le. Quality Map-guided Fidelity Compression Method for High-energy Regions of Spectral Data[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250650

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LIU Xiangli, LI Zan, CHEN Yifeng, CHEN Le. Quality Map-guided Fidelity Compression Method for High-energy Regions of Spectral Data[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250650

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Quality Map-guided Fidelity Compression Method for High-energy Regions of Spectral Data

doi: 10.11999/JEIT250650 cstr: 32379.14.JEIT250650

School of Communications, Xidian University, Xi’an 710071, China

Funds: National Natural Science Foundation of China (62531019),National Key R&D Program of China (2022YFC3301300), Innovative Research Groups of the National Natural Science Foundation of China (62121001)

Accepted Date: 2025-12-01
Rev Recd Date: 2025-12-01

Available Online: 2025-12-09

Abstract

Abstract

Objective In the context of intelligent evolution in communication and radar technologies, the inefficiency of radio frequency (RF) data compression has become a critical bottleneck restricting the expansion of transmission bandwidth and the improvement of system energy efficiency. Traditional compression methods struggle to balance compression ratio and reconstruction accuracy in complex scenarios with non-uniform energy distribution. This study aims to address the challenge of fidelity compression for spectral data with non-uniform energy distribution by developing an innovative method guided by a quality map to preserve high-energy regions, thereby enhancing the adaptability of RF signal processing in complex environments. Methods The proposed method employs a three-dimensional energy mask to dynamically guide the encoder to enhance features in high-energy regions, combined with multi-level complex convolution and inverted residual connections for efficient feature extraction and reconstruction. Key components include: Quality map: Derived from RF signals’ local energy and amplitude changes, fusing energy proportion and variation as structured prior. Loss function: Rate-distortion joint optimization integrating WMSE, complex correlation, and phase difference losses, with learnable parameters balancing objectives (Fig. 1).Compression network: Encoder-decoder framework with quality map extractors, deep encoders/decoders, and entropy coding, using complex convolution, residual spatial transformation (multi-scale features, high-frequency details), and gated normalization (low-energy noise suppression) (Figs. 2–6). Results and Discussions Experiments on the public dataset RML2018.01a demonstrate the superiority of the proposed method:Reconstruction Accuracy: Visual comparisons of real/imaginary parts and amplitude spectra show high overlap between reconstructed and original signals (Figs. 7–8), with errors concentrated in low-energy regions. PSNR remains ≥35 dB across the tested –4–20 dB SNR range, ensuring robust performance even in extreme low-SNR conditions (Fig. 9).Ablation Experiments: Removing the quality map guidance mechanism leads to significant reconstruction errors in high-energy regions, as reflected by lower PSNR, higher mean relative error (MRE), and reduced correlation coefficients compared to the full algorithm (Figs. 9), validating the quality map’s critical role in protecting high-energy features.Comparative Analysis: Compared with traditional methods (LFZip, CORAD), the proposed method achieves superior performance at -4 dB SNR: higher PSNR (35.75 dB vs. ≤29.45 dB), lower MRE (6.91% vs. ≥8.45%), and stronger correlation coefficients (0.898 vs. ≤0.832), with a slightly lower compression ratio (Table 1).Self-built Dataset Validation: To verify adaptability to practical complex scenarios, supplementary experiments were conducted on a MATLAB-simulated dataset (Table 3): 5 modulations (BPSK, QPSK, 8PSK, 16QAM, 64QAM), AWGN+Rayleigh fading channel, –4–20 dB SNR (step 6 dB), 25k samples (1k per modulation per SNR), 8:1:1 split. Even under fading channels, the method outperforms baselines at –4 dB SNR: PSNR 34.61 dB (vs. 28.46/27.88 dB), MRE 7.53% (vs. 9.00%/9.38%), correlation 0.885 (vs. 0.821/0.808; Table 3), with optimal rate-distortion performance across all compression ratios (Fig. 11).Slight performance reduction vs. RML2018.01a is attributed to Rayleigh fading-induced energy dispersion, but consistent superiority across datasets confirms the method’s strong robustness to non-uniform energy distribution and complex channel characteristics in practical applications. Conclusions This study presents a quality map-guided fidelity compression method for RF data in the frequency domain, addressing the challenge of non-uniform energy distribution. The method efficiently preserves high-energy region features through dynamic feature enhancement and multi-dimensional loss optimization. Experimental results highlight its advantages in reconstruction accuracy and noise resistance, providing a new framework for high-fidelity compression of complex RF signals in communication and radar systems. Future work will focus on extending the method to real-time processing scenarios and integrating physical layer constraints to further improve practical applicability.
- Quality map,
- Spatial feature transformation,
- Data compression,
- High-energy region fidelity

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References(28)

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