Wavelet Transform-Based Image Quantization Coding and Decoding

This paper presents an image quantization coding and decoding approach based on wavelet transform, employing the zerotree coding method. This technique effectively encodes and decodes images while preserving their crucial characteristics. During the encoding process, the image undergoes decomposition through wavelet transform, followed by quantization of the resulting coefficients. The implementation typically involves applying discrete wavelet transform (DWT) using filters like Haar or Daubechies wavelets, then quantizing coefficients through uniform or non-uniform quantization methods. Subsequently, the quantized coefficients are encoded using zerotree coding, which efficiently represents wavelet coefficient trees by identifying insignificant coefficients across scales, enabling high compression ratios. The decoding process reconstructs the quantized coefficients by decoding the zerotree representation, followed by inverse wavelet transform to restore the original image. The inverse transform implementation carefully reconstructs approximation and detail coefficients using synthesis filters matching the initial decomposition. Experimental results demonstrate that this method achieves substantial compression ratios while maintaining satisfactory image quality, with typical implementations showing significant bit-rate reduction compared to traditional compression techniques.