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Start with **Python for Data Science, Statistics, and Linear Algebra.** Understanding these basics is crucial for advanced t
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data compression.ppt tree structure vector
- 1. NADAR SARASWATHI COLLEGE OF ARTS & SCIENCE (AUTONOMOUS), THENI. Data Compression Tree structured vector quantizers By M.Vidhya M.SC(CS)
- 2. Introduction to Vector Quantization (VQ): Definition: Vector Quantization (VQ) is a technique for compressing data by partitioning it into clusters and representing each cluster with a centroid. Applications: Used in image compression, speech processing, and pattern recognition. Limitations of standard VQ: High computational complexity in encoding due to exhaustive search.
- 3. Tree-Structured Vector Quantization (TSVQ) Overview: Definition TSVQ is an efficient hierarchical method of VQ that reduces search complexity using a tree structure. Why TSVQ? Overcomes the computational inefficiency of flat-codebook VQ by organizing codewords in a tree format.
- 4. Structure of TSVQ: Root Node: Represents the entire data space. Internal Nodes: Divide data space into smaller regions. Leaf Nodes: Contain the final quantized codewords. Example Diagram: Tree structure representation.
- 5. Algorithm & Steps of TSVQ: 1.Tree Construction: 1. Start with a large dataset and split into two groups. 2. Recursively split each group until a stopping criterion is met (e.g., number of clusters). 2.Encoding Process: 1. Traverse the tree from the root to a leaf node. 2. Find the closest matching cluster for a given input vector. 3.Decoding Process: 1. Reconstruct the original data from the quantized codeword at the leaf node.
- 6. Advantages of TSVQ: Faster search time (O(log N) complexity vs. O(N) for full search VQ). Efficient storage due to hierarchical structure. Adaptive and scalable for large datasets.
- 7. Disadvantages of TSVQ: Suboptimal performance compared to full-search VQ. Tree pruning and optimization are required for best efficiency. Sensitive to training data and initial tree construction.
- 8. Applications of TSVQ: Image Compression: Reduces storage and transmission requirements. Speech Processing: Used in low-bit-rate speech coders. Pattern Recognition: Applied in machine learning and clustering tasks. Neural Networks: Used for efficient encoding of high-dimensional data.
- 9. Thank you