Image coding using vector quantization: a review
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Title: Image coding using vector quantization: a review

Section 1: INTRODUCTION
Description 1: Introduce the importance of image compression, the general techniques of image coding, and specifically focus on the use of vector quantization for image coding.

Section 2: DEFINITION OF VECTOR QUANTIZATION
Description 2: Provide a detailed definition of vector quantization, its components, and basic principles.

Section 3: VECTOR QUANTIZER DESIGN
Description 3: Discuss the goal, process, and methods used in designing optimal vector quantizers, including techniques like the LBG algorithm and simulated annealing.

Section 4: SPATIAL VECTOR QUANTIZERS (SVQ)
Description 4: Review the application of vector quantizers in the spatial domain for digital images, including adaptive and nonadaptive designs, methods of mean normalization, and classified vector quantizers.

Section 5: TRANSFORM VECTOR QUANTIZER (TVQ)
Description 5: Explain the concept of transform coding and its application using vector quantization, including adaptive transform coding, one-dimensional and two-dimensional transform vector quantizers, and interframe transform vector quantization.

Section 6: BINARY VECTOR QUANTIZER (BVQ)
Description 6: Explore vector quantization techniques specifically applied to binary images, such as facsimile coding and block truncation coding using binary vector quantization.

Section 7: SUBBAND VECTOR QUANTIZERS (SUBBAND VQ)
Description 7: Discuss the use of subband filtering in conjunction with vector quantization to exploit the human visual system's frequency sensitivity for image coding.

Section 8: CONCLUSION
Description 8: Summarize the various vector quantization techniques reviewed, their advantages, challenges, and potential areas for future research.