Digital images are composed of pixels, and lossless and lossy compression techniques are used to reduce the amount of data. Compression in the JPEG format involves preprocessing, DCT, quantization, and encoding. Digital images are used in a variety of fields, including photography, medical imaging, artificial intelligence, and more, with advances in quality enhancement and copyright protection.
A digital image is a digital representation of a photograph or drawing. These digital images are made up of pixels, the smallest unit of dots, each of which is assigned a value to represent brightness, color, etc. In general, the higher the number of pixels, the higher the resolution, but at the expense of the amount of data stored. To store and transmit these digital images efficiently, digital image compression techniques are needed to reduce the amount of data.
There are two types of digital image compression techniques: lossless compression and lossy compression. Lossless compression does not use any data loss during the compression process, so it is less efficient, but it can be restored to the same image as the original. Lossy compression, on the other hand, removes redundant or unnecessary data, making it difficult to restore the same image as the original, but it can achieve a high compression efficiency of many times to thousands of times higher than lossless compression, making it a popular compression technique.
JPEG, which we commonly use, is a representative digital image file format with lossy compression. The compression of JPEG format is mainly composed of preprocessing, DCT, quantization, and encoding.
First, preprocessing involves changing the color model and “sampling”. First, the color model of the digital image is changed from RGB to YCbCr. The RGB model combines the three primary colors of light to represent the color and brightness of a pixel together, while the YCbCr model separates the information of a pixel into Y, which represents brightness information, and Cb and Cr, which represent color information. When the color model is changed from the RGB model to the YCbCr model, sampling is performed to extract only some values from the pixels.
The human eye is sensitive to changes in brightness and relatively less sensitive to changes in color, so the sampling extracts all of the Y, which represents brightness information, and only part of the Cb and Cr, which represent color information, to the extent that the human eye cannot perceive changes in color. This sampling extracts information from a block of pixels in the ratio J:a
from a block of pixels in a certain ratio of J:a. Where J is the number of horizontal pixels in the pixel block, a is the number of pixels of information from the first row of the pixel block, and b is the number of pixels of information from the second row. For example, if you sample color information in a ratio of 4:2:0, the first row of a pixel block with 4 horizontal pixels extracts 2 pieces of color information, and the second row extracts no color information. In the end, only two of the eight colors in the 4×2 block are extracted, reducing the amount of data.
After the preprocessing, a transformation called DCT is performed. DCT is a process that converts the information in the sampled pixels into frequencies and represents the data as regularly separated data in the frequency domain. For efficiency, DCT is performed on a matrix blocked into 8 pixels horizontally and 8 pixels vertically as the basic unit. When DCT is performed, low-frequency components, which represent small differences in information between neighboring pixels, are gathered at the top left of the matrix, and high-frequency components, which represent large differences, are gathered at the bottom right of the matrix, and are represented as matrix values separated along the frequency domain. The cutoff value of the low-frequency component is larger than the cutoff value of the high-frequency component.
The next step is quantization. In the quantization process, the matrix value obtained by DCT is divided by a certain preset constant and rounded. In this case, the matrix value of the low-frequency component is divided by a small constant and rounded up, but the matrix value of the high-frequency component is divided by a large constant and rounded up to make it a value of zero. This is to reduce the size of the data by reducing the absolute value of the low-frequency components and removing the high-frequency components, considering that the human eye is sensitive to low-frequency components but less sensitive to high-frequency components.
Finally, the data is encoded. Encoding is the binary representation of the quantized matrix values. Huffman encoding is typically used for this process. Huffman encoding works by assigning fewer bits to represent data that occurs frequently and more bits to represent data that occurs infrequently. As a result, the Huffman encoding process can reduce the amount of data in a digital image without losing any data.
Digital images are utilized in many areas of our daily lives. For example, they are used in photography, medical imaging, satellite imagery, graphic design, product images in online stores, and many other applications. With the increasing use of digital images, the technologies involved in image processing are also rapidly evolving. In particular, advances in image recognition using artificial intelligence (AI) and machine learning (ML) have enabled innovative technologies such as facial recognition, self-driving cars, and smart cameras.
Along with the utilization of digital images, an important issue is the issue of copyright. As digital images become easier to copy and distribute, legal and technological measures are required to protect copyright and prevent infringement. For example, digital watermarking technology is used to embed copyright information in images in an invisible form to prevent illegal copying of images.
Research is also underway to improve the quality of digital images. A variety of image processing techniques are being developed, including techniques to create high-resolution images, noise removal, and color correction. These techniques help to make digital images sharper and more accurate.
Digital images play an important role in many aspects of our lives, and their importance will continue to grow in the future, so it’s important to understand and be able to properly utilize the technologies associated with digital images. Digital images are more than just photographs or drawings; they have a profound impact on our daily lives and technological advancements.
