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How to implement image compression algorithm in C#

How to implement image compression algorithm in C

#Abstract: Image compression is an important research direction in the field of image processing. This article will introduce the implementation of image compression in C# algorithm and give corresponding code examples.

Introduction:
With the widespread application of digital images, image compression has become an important part of image processing. Compression can reduce storage space and transmission bandwidth, and improve the efficiency of image processing. In the C# language, we can compress images by using various image compression algorithms. This article will introduce two common image compression algorithms: Run-Length Encoding (RLE) and Lempel-Ziv-Welch (LZW), and give corresponding C# code examples.

Run-Length Encoding (RLE) algorithm
Run-Length Encoding (RLE) algorithm is a simple and effective image compression algorithm. Its principle is to convert a sequence of consecutive repeating color values Represented as a count value and corresponding color value. The following is a C# code example that implements the RLE algorithm:

public byte[] RleCompress(byte[] image) { List compressedImage = new List(); int count = 1; byte current = image[0]; for (int i = 1; i < image.Length; i++) { if (image[i] == current) { count++; } else { compressedImage.Add((byte)count); compressedImage.Add(current); count = 1; current = image[i]; } } compressedImage.Add((byte)count); compressedImage.Add(current); return compressedImage.ToArray(); } public byte[] RleDecompress(byte[] compressedImage) { List decompressedImage = new List(); for (int i = 0; i < compressedImage.Length; i += 2) { byte count = compressedImage[i]; byte color = compressedImage[i + 1]; for (int j = 0; j < count; j++) { decompressedImage.Add(color); } } return decompressedImage.ToArray(); }

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Lempel-Ziv-Welch (LZW) algorithm
Lempel-Ziv-Welch (LZW) algorithm is a commonly used A lossless compression algorithm that uses a dictionary to store existing strings and replace repeated strings with corresponding index values. The following is a C# code example that implements the LZW algorithm:

public byte[] LzwCompress(byte[] image) { Dictionary dictionary = new Dictionary(); List compressedImage = new List(); string current = image[0].ToString(); for (int i = 1; i < image.Length; i++) { string next = current + image[i]; if (dictionary.ContainsKey(next)) { current = next; } else { compressedImage.Add(dictionary[current]); dictionary.Add(next, dictionary.Count + 1); current = image[i].ToString(); } } compressedImage.Add(dictionary[current]); byte[] compressedBytes = new byte[compressedImage.Count * 2]; for (int i = 0; i < compressedImage.Count; i++) { compressedBytes[i * 2] = (byte)(compressedImage[i] >> 8); compressedBytes[i * 2 + 1] = (byte)(compressedImage[i] & 0xff); } return compressedBytes; } public byte[] LzwDecompress(byte[] compressedImage) { Dictionary dictionary = new Dictionary(); List decompressedImage = new List(); int nextCode = 256; for (int i = 0; i < nextCode; i++) { dictionary.Add(i, ((char)i).ToString()); } int current = (compressedImage[0] << 8) + compressedImage[1]; decompressedImage.AddRange(Encoding.Default.GetBytes(dictionary[current])); for (int i = 2; i < compressedImage.Length; i += 2) { int code = (compressedImage[i] << 8) + compressedImage[i + 1]; if (!dictionary.ContainsKey(code)) { string entry = dictionary[current] + dictionary[current][0]; dictionary.Add(code, entry); decompressedImage.AddRange(Encoding.Default.GetBytes(entry)); } else { decompressedImage.AddRange(Encoding.Default.GetBytes(dictionary[code])); } current = code; } return decompressedImage.ToArray(); }

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Conclusion:
This article introduces two algorithms for image compression in C#: Run-Length Encoding (RLE) and Lempel- Ziv-Welch (LZW). By implementing the corresponding compression and decompression functions, we can compress and decompress images. These algorithms are commonly used compression algorithms in image processing, which can help us reduce storage space and transmission bandwidth, and improve the efficiency of image processing.

Reference:

Run-Length Encoding. Wikipedia.（https://en.wikipedia.org/wiki/Run-length_encoding）
Lempel- Ziv-Welch. Wikipedia.（https://en.wikipedia.org/wiki/Lempel-Ziv-Welch）

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