WSQ

WSQ (Wavelet Scalar Quantization) is a commonly used lossy algorithm for still image compression. It was adopted by the Federal Bureau of Investigation (FBI) in 1994 as the standard for biometric identification. This algorithm is also widely used in financial transactions and medical imaging applications.WSQ is an improved version of the wavelet transform. Wavelet transform can decompose a signal into a set of signals at varying frequencies and then compress the signal. It can provide excellent compression rates while maintaining image clarity. The WSQ technique uses scalar quantization to further reduce the file size. It also involves a form of data hiding. By exploiting the correlation between adjacent pixels, several redundant image pixels are hidden in a single quantized data value.The working principle of WSQ is very simple. It starts by decomposing an image into a set of scaled wavelet components. These components are further sub-sampled and merged to form blocks of information. The block size is typically 11 x 11 pixels. The coefficients are then transformed using a discrete cosine transform (DCT). The result is a set of integer coefficients. Finally, the coefficients are quantized using a uniform or non-uniform quantizer and stored in an output file.WSQ is an ideal compression format for digital images because of its ability to compress data at a high ratio while maintaining a high level of quality. It can achieve a compression ratio of up to 20:1, while keeping the image resolution and quality near the same as the original. WSQ is ideal for applications where low file size and fast transmission of data are important. Examples include fingerprint images, ID photos, passport photos, medical images, X-ray images, satellite images, and digital surveillance images.Overall, WSQ is a very efficient and effective compression format for digital images. It offers higher compression rates than other image formats such as JPEG and TIFF. It provides excellent image quality and is well-suited for applications where small file size and fast data transmission are critical.