Yes, it is possible to implement Huffman image compression without using built-in functions. Here are the general steps involved in Huffman image compression:

1. Divide the image into blocks or pixels.

2. Calculate the frequency of occurrence of each block or pixel in the image.

3. Create a Huffman tree using the frequency information. This involves building a binary tree where each leaf node represents a block or pixel and its frequency, and each internal node represents the sum of the frequencies of its child nodes.

4. Assign a binary code to each block or pixel based on its location in the Huffman tree. This involves traversing the tree and assigning a 0 or 1 bit to each edge of the tree, depending on whether the left or right child node is followed.

5. Replace each block or pixel in the image with its corresponding binary code.

6. Save the Huffman tree and the binary code representation of the image to a file.

To implement Huffman image compression without using built-in functions, you would need to write custom code to perform each of these steps. This would involve writing code to read and write image files, calculate block or pixel frequencies, build the Huffman tree, assign binary codes, and replace the blocks or pixels in the image with their binary codes.

While implementing Huffman image compression without built-in functions can be a challenging task, it can also be a rewarding learning experience and can help you develop a deeper understanding of how image compression works.

We map each symbol in the Huffman code to a unique binary code, and then encoding the binary code into the image pixels. Program in Python:

```python

import heapq

import numpy as np

import cv2

# Define the Huffman code dictionary

huffman_codes = {'A': '00', 'B': '01', 'C': '10', 'D': '11'}

# Define the grayscale image data

image_data = np.array([[128, 64, 192], [0, 255, 128], [64, 0, 192]], dtype=np.uint8)

# Convert the image data to a flattened array

image_flat = image_data.flatten()

# Count the frequency of each symbol in the image data

symbol_freq = {k: 0 for k in huffman_codes.keys()}

for symbol in image_flat:

symbol_freq[chr(symbol)] += 1

# Build the Huffman tree using a priority queue

heap = [[freq, [sym, ""]] for sym, freq in symbol_freq.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:])

# Create the Huffman code dictionary from the Huffman tree

huffman_codes = {pair[0]: pair[1] for pair in heap[0][1:]}

# Encode the image data using the Huffman codes

encoded_data = ""

for symbol in image_flat:

encoded_data += huffman_codes[chr(symbol)]

# Convert the binary encoded data to a bytearray

byte_array = bytearray()

for i in range(0, len(encoded_data), 8):

byte_array.append(int(encoded_data[i:i+8], 2))

# Reshape the byte array to match the original image data shape

encoded_image_data = np.array(byte_array).reshape(image_data.shape)

# Save the encoded image to a file

cv2.imwrite("encoded_image.png", encoded_image_data)

```

It reshapes the bytearray to match the original image data shape and saves the encoded image to a file.

The program assumes that the Huffman code dictionary maps each symbol to a unique binary code. If the Huffman code dictionary is not optimal or does not map each symbol to a unique binary code, the program may not produce the expected results.

Hope it helps.

Credit AI tools