Article Details

Abstract

To address the high computational complexity of traditional Linde-Buzo-Gray Vector Quantization (LBG-VQ), this paper investigates low-complexity predictive vector quantization algorithms. Based on the analysis of boundary pixel prediction algorithms, including Direct Predicted Vector Quantization (DPVQ), Boundary Predicted Vector Quantization (BPVQ), and Weighted Boundary Predicted Vector Quantization (WBPVQ), a Distance-Weighted Boundary Predicted Vector Quantization (DWBPVQ) algorithm is proposed by leveraging pixel distance and spatial correlation to enhance prediction coding accuracy. Simulation experiments of each algorithm are conducted on a 512×512 Lena grayscale image in the MATLAB environment. Compared with the traditional LBG-VQ algorithm, the proposed algorithms achieve significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) under similar encoding time. Among them, the DWBPVQ scheme obtains the optimal coding performance with a PSNR of 24.72 dB and an SSIM of 0.3421, which verifies that the algorithm effectively improves image coding quality while maintaining low complexity.

Keywords

image coding; vector quantization; predictive vector quantization; boundary prediction; distance weighting

References

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About the authors

Liu Yilin (May 1990-), male, Han ethnicity, from Lanzhou, Gansu Province, is a senior engineer with a PhD. His research interests include image processing and signal processing.