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

Received Date: 2025-07-09
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, inefficiency in Radio Frequency (RF) data compression represents a critical bottleneck that restricts transmission bandwidth expansion and system energy efficiency improvement. Conventional compression methods fail to balance compression ratio and reconstruction accuracy in complex scenarios characterized by non-uniform energy distribution. This study aims to address fidelity compression of spectral data with non-uniform energy distribution by developing a quality map-guided method that preserves high-energy regions and improves the adaptability of RF signal processing in complex environments. Methods A quality map-guided fidelity compression method is proposed. A three-dimensional energy mask is constructed to dynamically guide the encoder and enhance features in high-energy regions. Multi-level complex convolution and inverted residual connections are adopted for efficient feature extraction and reconstruction. The quality map is derived from local energy and amplitude variations of RF signals by fusing energy proportion and variation as structured prior information. A rate–distortion joint optimization loss function is designed by integrating weighted mean squared error, complex correlation loss, and phase difference loss, with learnable parameters used to balance competing objectives (Fig. 1). The compression network follows an encoder–decoder framework that incorporates quality map extractors, deep encoders and decoders, and entropy coding. Complex convolution, residual spatial feature transformation for multi-scale and high-frequency feature preservation, and gated normalization for low-energy noise suppression are employed (Figs. 2–6). Results and Discussions Experiments conducted on the public dataset RML2018.01a demonstrate the superiority of the proposed method. Reconstruction accuracy: Visual comparisons of real and imaginary components and amplitude spectra show strong overlap between reconstructed and original signals (Figs. 7–8), with reconstruction errors mainly concentrated in low-energy regions. The Peak Signal-to-Noise Ratio (PSNR) remains ≥35 dB across the tested –4 to 20 dB signal-to-Noise Ratio (SNR) range, confirming robust performance even under extremely low signal-to-noise conditions (Fig. 9). Ablation experiments: Removal of the quality map guidance mechanism results in significant reconstruction errors in high-energy regions, reflected by lower PSNR, higher Mean Relative Error (MRE), and reduced correlation coefficients compared with the complete method (Fig. 9). These results confirm the critical role of the quality map in preserving high-energy features. Comparative analysis: Relative to conventional methods, including LFZip and CORAD, the proposed method achieves superior performance at –4 dB SNR, with 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), at the expense of a slightly lower compression ratio (Table 1). Self-built dataset validation: To evaluate adaptability to practical complex scenarios, supplementary experiments are performed using a MATLAB-simulated dataset (Table 3) comprising five modulation schemes (BPSK, QPSK, 8PSK, 16QAM, and 64QAM), an additive white Gaussian noise plus Rayleigh fading channel, SNRs from –4 to 20 dB with a 6 dB step, 25,000 samples, and an 8:1:1 data split. Under fading channels, the proposed method continues to outperform baseline methods at –4 dB SNR, achieving a PSNR of 34.61 dB (vs. 28.46/27.88 dB), MRE of 7.53% (vs. 9.00%/9.38%), and a correlation coefficient of 0.885 (vs. 0.821/0.808; Table 3), with optimal rate–distortion performance observed across all compression ratios (Fig. 11). The slight performance degradation relative to RML2018.01a is attributed to Rayleigh fading-induced energy dispersion. Consistent superiority across datasets confirms strong robustness to non-uniform energy distribution and complex channel characteristics in practical applications. Conclusions A quality map-guided fidelity compression method for frequency-domain RF data is presented to address challenges caused by non-uniform energy distribution. High-energy region features are effectively preserved through dynamic feature enhancement and multi-dimensional loss optimization. Experimental results demonstrate advantages in reconstruction accuracy and noise resistance, providing a viable framework for high-fidelity compression of complex RF signals in communication and radar systems. Future work will extend the method to real-time processing scenarios and incorporate physical-layer constraints to further enhance practical applicability.
- Quality map,
- Spatial feature transformation,
- Data compression,
- High-energy region fidelity

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References(25)

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