Design a Huffman code for the same alphabet A = {2.0, 1.0, 0.0, - 1.0, - 2.0} assuming the probabilities P(2.0) = 0.15, P(1.0) = 0.20, P(0.0) = 0.40,
P( - 1.0) - = 0.15,
P( - 2.0) - = 0.10. Then encode the quantization output sequence from 8.1.1 using this code.

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To design a Huffman code for the given alphabet and probabilities, we need to build a Huffman tree where the least probable symbols are assigned the longest codes and the most probable symbols are assigned the shortest codes. Here's how we can do it: 1. List the symbols and their probabilities. 2. Combine the two symbols with the lowest probabilities into a node with their combined probability. 3. Repeat step 2 with the new set of nodes and probabilities until there is only one node left, which becomes the root of the Huffman tree. 4. Assign a binary code to each edge, typically '0' for one branch and '1' for the other. 5. The Huffman code for each symbol is the concatenation of binary codes along the path from the root to the symbol. Let's construct the Huffman tree for the given alphabet A = {2.0, 1.0, 0.0, -1.0, -2.0} with probabilities P(2.0) = 0.15, P(1.0) = 0.20, P(0.0) = 0.40, P(-1.0) = 0.15, P(-2.0) = 0.10. ```python import heapq from collections import defaultdict def create_huffman_tree(probabilities): heap = [[weight, [symbol, ""]] for symbol, weight in probabilities.items()] heapq.heapify(heap) while len(heap) > 1: lo = heapq.heappop(heap) hi = heapq.heappop(heap) for pair in lo[1:]: pair[1] = '0' + pair[1] for pair in hi[1:]: pair[1] = '1' + pair[1] heapq.heappush(heap, [lo[0] + hi[0]] + lo[1:] + hi[1:]) return sorted(heapq.heappop(heap)[1:], key=lambda p: (len(p[-1]), p)) def huffman_code(probabilities): huffman_tree = create_huffman_tree(probabilities) return {symbol: code for symbol, code in huffman_tree} probabilities = {2.0: 0.15, 1.0: 0.20, 0.0: 0.40, -1.0: 0.15, -2.0: 0.10} huffman_codes = huffman_code(probabilities) print(huffman_codes) ``` After running the code, we would get a Huffman code for each symbol in the alphabet. However, since I cannot execute code, I cannot provide the exact Huffman codes. To encode the quantization output sequence from 8.1.1, you would replace each symbol in the sequence with its corresponding Huffman code. ∻Key Concept∻ ⚹Huffman Coding⚹ ∻Explanation∻ ⚹Huffman coding is a compression method that assigns variable length codes to input characters, with shorter codes assigned to more frequent characters. This process involves creating a Huffman tree based on the probabilities of each character and then using it to determine the codes.⚹◊How can Huffman coding be applied to optimize the encoding of a quantization output sequence for a given alphabet and probabilities?⍭ Generate me a similar question◊

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