Compression in databases and data processing can significantly affect performance in several ways. Firstly, it reduces the amount of disk space required to store data, which can lead to cost savings on storage infrastructure. Secondly, because compressed data occupies less space, it can be read from and written to disk more quickly, potentially improving the performance of data-intensive operations. However, this comes with a trade-off: compressed data must be decompressed before it can be processed, which can incur additional CPU overhead. In scenarios where read and write speeds are bottlenecks, the performance gains from reduced disk I/O can outweigh the cost of decompression. But in CPU-bound scenarios, the extra processing required for decompression might negatively impact performance. The effectiveness of compression in improving database performance thus depends on the specific use case, data access patterns, and hardware configuration.

Common techniques for compressing text files include Huffman coding and Lempel-Ziv-Welch (LZW) compression. Huffman coding is a form of lossless data compression that assigns shorter codes to frequently occurring characters and longer codes to less frequent characters. This is based on the principle that reducing the size of more common characters leads to significant overall compression. LZW compression, another lossless technique, builds a dictionary of frequently occurring phrases and replaces them with shorter codes. It's particularly effective for text files due to the repetitive nature of words and phrases. Both techniques are efficient because they exploit the redundancy inherent in text data, significantly reducing file size without losing information. They are widely used in formats like ZIP files and GIF images.

Different compression techniques can have a significant impact on the transmission of data over the internet, particularly in terms of speed and bandwidth usage. Lossless compression, while preserving the entirety of the original data, typically offers moderate reduction in file size, thus moderately improving transmission speeds and reducing bandwidth usage. It's often used for text and data files where integrity is crucial. Lossy compression, on the other hand, can greatly reduce file sizes by sacrificing some quality, leading to much faster transmission and significantly lower bandwidth consumption. This is particularly beneficial for multimedia content like images, videos, and audio, where a balance between quality and speed is essential. The use of these techniques helps in accommodating bandwidth limitations and improving user experience by reducing loading times and buffering. Additionally, efficient compression algorithms enable more data to be transmitted within the same bandwidth, which is crucial for streaming services, online gaming, and other bandwidth-intensive applications.

Run-Length Encoding (RLE) is a simple form of data compression in which sequences of the same data value (runs) are stored as a single data value and count. For example, the string "AAAAABBBCCDAA" would be encoded as "5A3B2C1D2A", significantly reducing the size if the runs are long. RLE is most effective with data that contains many such runs, making it ideal for certain types of graphic files like bitmaps, where many pixels of the same colour can be found adjacent to each other. However, RLE is less effective for files like photographs or detailed images, where pixel colours change frequently. Its simplicity makes it fast and easy to implement, but the compression ratio is highly dependent on the nature of the data. RLE is also used in fax machines and simple image formats like BMP and TIFF.

Lossy and lossless compression are two distinct methods of reducing file sizes, each impacting quality and size differently. Lossless compression reduces file size without losing any original data, making it ideal for text files, software, and certain types of image and sound files where preserving the original quality is crucial. Techniques like ZIP or PNG use lossless compression. The file, when uncompressed, is identical to the original. Lossy compression, on the other hand, significantly reduces file size by permanently removing some data, especially redundant or less significant information. This is common in JPEG images and MP3 audio files. While lossy compression achieves higher compression ratios, it does so at the cost of quality, which can become noticeable at higher compression levels. The choice between lossy and lossless compression depends on the required balance between file size and fidelity. For instance, lossless is preferred for archival purposes, while lossy is suitable for streaming media where bandwidth is limited.