Image Compression is the art and Science of reducing the amount of data required to represent an image. It is one of the useful and commercially successful technique in the field of Digital Image Compression. The innumerable images are compressed and decompressed daily. Image compression techniques are classified into lossless and lossy compression techniques. This paper covers three lossy compression techniques such as Tree Structured Vector Quantization (TSVQ), TSVQ reduces the quantizer search complexity by replacing full search encoding with a sequence of tree decisions , and Multi Stage Vector Quantization (MSVQ) , Multistage Vector Quantization is a modification of Unconstrained Vector Quantization technique. It is also called as Multistep, Residual or Cascaded Vector Quantization. Multistage Vector Quantization (MSVQ) technique preserves all the features of Unconstrained Vector Quantization technique while decreasing the computational complexity, memory requirements and spectral distortion. And Side Match Vector Quantization (SMVQ), In SMVQ Neighbor pixels within an image are similar unless there is an edge across. But the topic of our interest is Side Match Vector Quantization.

SMVQ, MSVQ, TSVQ

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