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Why are JPEG images so small? Uncover the compression secret behind it!
In this era of rapid digital development, we are exposed to pictures all the time. Whether it is a photo shared on social media or an image displayed on a web page, the JPEG format is undoubtedly one of the most common image formats. But why can JPEG images be so small? This is no accident, but the result of a series of complex image compression technologies and algorithms.
Basic concepts of image compression
Image compression is a data compression technique designed to reduce the file size of digital images, thereby reducing the cost of storage or transmission. Compression algorithms exploit visual perception and statistical properties of image data to provide superior results over other general-purpose compression methods for digital data.
Lossy and lossless compression
Image compression can be divided into two categories: lossy compression and lossless compression. Lossy compressions are particularly suitable for natural images, such as photos, etc., as they allow significant file size reductions to be obtained within a certain range of distortion. For data of higher importance, such as medical images or technical drawings, lossless compression is usually chosen.
The most commonly used lossy compression method is the discrete cosine transform (DCT), which is widely used in the JPEG format.
JPEG compression mechanism
The JPEG format utilizes a variety of compression techniques to significantly reduce the size of image files. First, JPEG uses DCT to convert the image from the spatial domain to the frequency domain. Such transformations allow us to more efficiently identify and compress image details to which the human eye is less sensitive.
Through the "chroma subsampling" method, the JPEG format can ignore the human eye's sensitivity to certain color changes, significantly reducing the amount of data required.
Color Quantization
Another important compression technique is color quantization, a process that simplifies the color space by selecting a few representative colors. Each pixel only needs to reference the color index in the compressed image header, thus compressing the amount of data required for each pixel.
Evolution of compression algorithms
With the development of science and technology, many new compression methods such as wavelet transform and machine learning technology have been proposed. These advanced methods can further improve compression effects and image quality, significantly promoting the advancement of digital image processing technology.
Huffman coding is an important compression technique that can efficiently encode data based on the frequency of occurrence of symbols.
Use of METADATA
With the advancement of image compression technology, images can also contain some metadata. These metadata include image classification, search and browsing information, further improving the flexibility and functionality of the JPEG format.
Compression quality and processing capabilities
Although there is often a trade-off between image compression ratio and quality, readers' subjective evaluation of image quality is sometimes one of the most critical indicators. Therefore, the quality of compression often depends on the final visual effect rather than a purely numerical evaluation.
Conclusion
The development of the JPEG format has made the dissemination of digital images more convenient and popular. However, the compression technology behind this is still full of challenges and opportunities. More and better image compression methods may appear in the future. So, what will be the next generation of image compression technology?
