Using vector quantization for image processing

Using Vector Quantization for Image Processing

Image compression is the process of reducing the number of bits required to represent an image. Vector quantization, the mapping of pixel intensiry vectors into binary vectors indexing a limited number of possible reproductions, is a popular image compression algorithm. Compression has traditionally been done with little regard for image processing operations that may precede or follow the comp...

Vector Quantization for Image Compression-Concept Quantization

Image Compression Using Hybrid Vector Quantization

In this paper, image compression using hybrid vector quantization scheme such as Multistage Vector Quantization (MSVQ) and Pyramid Vector Quantization (PVQ) are introduced. A combined MSVQ and PVQ are utilized to take advantages provided by both of them. In the wavelet decomposition of the image, most of the information often resides in the lowest frequency subband. MSVQ is applied to significa...

Image Compression Using Learned Vector Quantization

This paper presents a study and implementation of still image compression using learned vector quantization. Grey scale, still images are compressed by 16:1 and transmitted at 0.5 bits per pixel, while maintaining a peak signal-to-noise ratio of 30 dB. The vector quantization is learned using Kohonen’s self organizing feature map (SOFM). While not only being representative of the training set, ...

Lossless Index Coding for Image Vector Quantization Using Huffman Codes

Image vector quantization (VQ) has many current and future envisioned applications, such as digital image and signal compression, watermarking, data hiding, speaker identification. The idea of lossless index coding is to enhance the performance of image VQ by further encoding the index map of the VQ without introducing extra distortion. A novel lossless index coding scheme, called Index Associa...