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JPEG (Joint Photographic Experts Group) compression is a lossy compression technique used to reduce the file size of digital images while maintaining an acceptable level of image quality. The compression works by reducing the amount of data in the image file, primarily by removing high-frequency information that is less important to the overall image.
Here's how JPEG compression works:
- Image preparation: The original image is prepared for compression by converting it from its original color space to the YUV color space. In the YUV color space, the brightness information is separated from the color information, which allows for more efficient compression.
- Dividing the image into blocks: The image is divided into 8x8 pixel blocks, and each block is processed independently.
- Discrete Cosine Transform (DCT): For each block, a mathematical operation called the Discrete Cosine Transform (DCT) is applied. The DCT converts the pixel values in the block from the spatial domain (pixels arranged in a grid) to the frequency domain (frequencies arranged in a grid).
- Quantization: The frequency domain values are then divided by a set of predefined values called the quantization matrix. This step is where much of the data is lost. The quantization matrix effectively rounds off the high-frequency components of the block's frequency spectrum, resulting in a loss of detail.
- Huffman coding: The resulting values are then compressed using Huffman coding, a lossless compression technique. Huffman coding assigns shorter codes to more frequently occurring values and longer codes to less frequently occurring values, resulting in further compression.
- Image reconstruction: To display or print the compressed image, the reverse process is applied. The Huffman-coded values are decoded, the quantized values are multiplied by the quantization matrix, and the inverse DCT is applied to convert the frequency domain values back to the spatial domain.
The degree of compression and resulting loss of image quality can be controlled by adjusting the quantization matrix used in step 4. Higher levels of compression result in a greater loss of detail but also produce smaller file sizes. Lower levels of compression result in larger file sizes but retain more detail.