Simultaneous Reversible Data Hiding and Quality Enhancement for VQ-Compressed Images via Quality Improvement Codes

With the rapid proliferation of digital multimedia in resource-constrained Internet of Things (IoT) environments, there is growing demand for efficient image compression combined with secure data embedding. Existing Vector Quantization (VQ)-based Reversible Data Hiding (RDH) methods prioritize embedding capacity while neglecting reconstruction fidelity, often introducing noticeable quality degradation in edge regions—unacceptable for high-fidelity applications such as medical imaging and forensic analysis. This paper proposes a lightweight RDH framework with a once-offline trained VQ codebook that simultaneously performs secure data embedding and visual quality enhancement for VQ-compressed images. Quality Improvement Codes (QIC) are generated from pixel-wise residuals between original and VQ-decompressed images and embedded into the VQ index table using a novel Recoding Index Value (RIV) mechanism without additional transmission overhead. Sobel edge detection identifies perceptually sensitive blocks for targeted enhancement. Comprehensive experiments on ten standard test images across multiple resolutions (256 × 256, 512 × 512) and codebook sizes (64–1024) demonstrate Peak Signal-to-Noise Ratio (PSNR) gains of +4 to +5.39 dB and Structural Similarity Index Measure (SSIM) improvements of +4.12% to +9.86%, with embedding capacities approaching 100 Kbits. The proposed approach consistently outperforms existing methods in both image quality and payload capacity while eliminating computational overhead of deep learning models, making it highly suitable for resource-constrained edge devices and real-time multimedia security applications.