Wavelet-Based Image Compression Implementation

I have developed a custom wavelet-based image compression algorithm that implements both standard compression and sequential image compression techniques. Wavelet image compression represents a widely-used image processing methodology that transforms images into the wavelet domain, leveraging the unique characteristics of wavelet coefficients to achieve efficient compression and reconstruction. Standard compression involves processing the entire image as a single unit, while sequential compression applies compression individually to different segments or regions of the image. The implementation utilizes discrete wavelet transform (DWT) for domain conversion, followed by thresholding and quantization of wavelet coefficients to reduce data size. Key functions include wavelet decomposition using filters like Daubechies or Haar wavelets, coefficient thresholding to eliminate insignificant values, and entropy encoding for final compression. This algorithm enables high-efficiency image compression with flexible reconstruction capabilities, significantly reducing storage requirements and enhancing image transmission speeds while maintaining acceptable visual quality through proper inverse wavelet transform implementation.