数据压缩算法分为哪两类_deflate压缩算法[通俗易懂]

1.数据压缩算法DEFLATE

当前数据压缩领域流行的无损压缩算法有很多，如DEFLATE、BZIP2、LZMA、LZMA2等等，其中压缩速度最快的是DEFLATE压缩算法。根据对DEFLATE算法性能的实际测试，发现DEFLATE算法在大数据文件压缩方面的性能表现，并不如预期那样令人满意。

Deflate是同时使用了LZ77算法与哈夫曼编码（Huffman Coding）的一个无损数据压缩算法1。

Deflate压缩与解压代码可以在自由、通用的压缩库zlib上找到。常见的压缩算法如下：

• zlib（RFC1950）：一种格式，是对deflate进行了简单的封装，zlib=zlib头+deflate编码的实际内容+zlib尾。
• gzip（RFC1952）：一种格式，也是对deflate进行的封装，gzip=gzip头+deflate编码的实际内容+gzip尾。

LZ77算法是采用字典做数据压缩的算法，由以色列的两位大神Jacob Ziv与Abraham Lempel在1977年发表的论文《A Universal Algorithm for Sequential Data Compression》中提出。基于统计的数据压缩编码，比如Huffman编码，需要得到先验知识，即信源的字符频率，然后进行压缩。但是在大多数情况下，这种先验知识是很难预先获得。因此，设计一种更为通用的数据压缩编码显得尤为重要。LZ77数据压缩算法应运而生，其核心思想是利用数据的重复结构信息来进行数据压缩。

two types of compression methodologies:

• Stateless – data associated with a compression operation is compressed without any reference to another compression operation.
• Stateful – data in each compression operation is compressed with reference to previous compression operations in the same data stream i.e. history of data is maintained between the operations.

stateful inflate

• Inflate: 这是一个算法，通常用于数据压缩和解压缩。它是DEFLATE算法的一部分，DEFLATE是一个广泛使用的无损数据压缩算法。
• Stateful (有状态的): 在计算中，一个算法或过程如果依赖于其之前的状态或历史，我们说它是“有状态的”。与“无状态的”算法或过程相反，后者不依赖于任何之前的状态。

在解压缩过程中使用某种状态的系统或算法。这通常涉及到以下内容：

上下文敏感: 有状态的解压缩算法可能需要对之前的上下文进行敏感分析，以便更好地理解并解压缩数据。

历史记录: 这些算法可能会跟踪之前处理过的数据或模式，以优化解压缩过程。

动态调整: 算法可能会根据它之前遇到的数据动态地改变其行为或参数。

stateless inflate

Here is a simplified explanation of how stateless inflate works:

1. Block Independence: In a stateless inflate process, compressed data is divided into blocks, and each block is compressed independently. This means that the decompression of one block doesn’t rely on the decompression state of previous blocks.
2. Decompression Algorithm: The stateless inflate algorithm reads a compressed block of data and uses the information within that block to decompress it. This typically involves reconstructing the original data from the compressed form using the Huffman codes and LZ77 compression techniques.
3. No Persistent State: After decompressing a block, there is no need to maintain persistent state information for the next block. Each block is treated as an independent unit, making it easier to parallelize or distribute the decompression process.
4. Efficiency: Stateless inflate can be more efficient in certain scenarios, especially when dealing with streaming data or in parallel processing environments. It allows for a simpler and faster implementation because there is no need to manage state information across blocks.

Stateless inflate is commonly used in scenarios where data can be processed in a streaming fashion, and each compressed block is relatively small and independent. This approach is well-suited for certain applications, such as network protocols, file compression formats, and other situations where data arrives in chunks that can be processed independently.

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