# Decompress the data decompressed_data = discipline_zerozip.decompress(compressed_data)
assert data == decompressed_data The Discipline Zerozip algorithm can be implemented in a variety of programming languages. Here is a sample implementation in Python:
# Detect zero-filled blocks if self._is_zero_filled(block): compressed_data.extend(self._compress_zero_block(block)) else: compressed_data.extend(self._compress_non_zero_block(block)) discipline zerozip
# Sample data with zero-filled blocks data = b'\x00\x00\x00\x00\x00\x00\x00\x00' * 1024 + b'Hello, World!' + b'\x00\x00\x00\x00\x00\x00\x00\x00' * 512
def _compress_non_zero_block(self, block): # Compress the non-zero-filled block using RLE and entropy coding compressed_block = bytearray() i = 0 while i < len(block): count = 1 while i + 1 < len(block) and block[i] == block[i + 1]: i += 1 count += 1 compressed_block.extend(struct.pack('B', count)) compressed_block.extend(bytes([block[i]])) i += 1 return bytes(compressed_block) # Decompress the data decompressed_data = discipline_zerozip
return bytes(decompressed_data)
if block_type == 0: # Zero-filled block block_size = struct.unpack_from('H', compressed_data)[0] compressed_data = compressed_data[2:] decompressed_data.extend(bytes([0]) * block_size) else: # Non-zero-filled block block = self._decompress_non_zero_block(compressed_data) decompressed_data.extend(block) compressed_data = compressed_data[len(block):] Discipline Zerozip offers a simple, yet efficient approach
def _decompress_non_zero_block(self, compressed_block): decompressed_block = bytearray() i = 0 while i < len(compressed_block): count = struct.unpack_from('B', compressed_block, offset=i)[0] i += 1 byte = compressed_block[i] i += 1 decompressed_block.extend(bytes([byte]) * count) return bytes(decompressed_block) This implementation provides a basic example of the Discipline Zerozip algorithm. You may need to modify it to suit your specific use case. Discipline Zerozip offers a simple, yet efficient approach to lossless data compression. By leveraging zero-filled data blocks and RLE compression, it achieves competitive compression ratios with existing algorithms. The provided implementation demonstrates the algorithm's feasibility and can be used as a starting point for further development and optimization.