What is lossy image compression?

In today's world, a large amount of data is consumed and transferred daily. Due to this high usage, the efficient storage and transmission of data hold a great degree of importance for feasible communication. For this purpose, image compression techniques are deployed to reduce the size of image files, making them easier to manage and transfer. One of these compression techniques is lossy image compression.

Lossy image compression

Lossy image compression is a compression technique that achieves the reduction in file size by discarding some image data, that holds less importance with respect to the human eye perception. The name "lossy" suggests this loss in data. However, the data to be discarded is selected on some criteria, such that it doesn't deform the image much and all the important details are retained.

The process works by compressing the image through various techniques. The compressed image is not usable, so it is restored through decompression methods. However, in lossy compression, the data lost during compression is not restored during compression, leading to reduced file size.

Process of lossy image compression

The process of lossy image compression is iterative and depends upon various perception models that evaluate the image to identify various image components, consequently separating the important ones from the discardable details.

The algorithms used in the lossy image compression techniques eliminate redundancies, data repetition, and less noticeable visual elements. However, these algorithms preserve the essential structure and semantical information. Through this process, the file size is greatly reduced without severely affecting the perceived quality of the image.

Benefits of lossy image compression

Lossy image compression offers several advantages in various applications, such as:

1. Reduced file size: Lossy compression techniques help significantly reduce the size of image files, making it easier to store and share images across various platforms.

2. Efficient transmission: Smaller files are easier and faster to transmit and share over the internet, making it ideal for multimedia content platforms as well as users.

3. Storage efficiency: Smaller files take less space to store as compared to uncompressed files. This allows for more images to be stored in limited storage spaces.

Limitations of lossy compression techniques

However, there are several limitations to using lossy compression techniques

1. Image quality loss: The most significant drawback of lossy compression is the loss of image quality. As the compression algorithm discards non-essential image data, some visual details, textures, and fine gradients are lost. The extent of quality loss depends on the compression settings and the algorithm used.

2. Irreversible changes: Unlike lossless compression, where the original image can be perfectly reconstructed from the compressed version, lossy compression is not fully reversible. Once image data is discarded or altered during compression, it becomes challenging to recover the exact original image during decompression accurately.

3. Incompatibility with applications: Lossy compression might not be suitable for certain applications that demand high-fidelity images, such as medical imaging, archival purposes, or professional graphic design work.

Lossy image compression methods

There are various lossy image compression methods that incorporate this phenomenon.

Joint Photographic Experts Group (JPEG):

JPEG is a lossy image compression algorithm that is particularly suitable for compressing photographs and natural scenes.

JPEG achieves compression by using Discrete Cosine Transform (DCT) to transform the image data into frequency components. Then, it quantizes and discards high-frequency components based on the image quality settings chosen by the user.

The higher the compression ratio provided by the user, the more high-frequency components are discarded from the image, leading to a smaller file size but reduced image quality.

WebP compression

Developed by Google, WebP is a modern lossy image compression format that aims to provide better compression efficiency than JPEG.

WebP uses a combination of advanced compression techniques, such as predictive coding and variable length coding, to achieve smaller file sizes while maintaining good image quality. It also supports transparency, animation, and lossless compression for certain types of images.

WebP supports both lossy and lossless compression modes. In lossy mode, it reduces file sizes by selectively discarding image information, while the lossless mode retains all original image data, ensuring no loss in quality.

High-Efficiency Image Format (HEIF)

HEIF (High-Efficiency Image Format) is a modern image format developed by the Moving Picture Experts Group (MPEG) as part of the High-Efficiency Video Coding (HEVC) standard.

HEIF was adopted by Apple as the default image format for iOS devices starting with iOS 11. It offers significant improvements in compression efficiency over JPEG while supporting various advanced features.

Conclusion

Lossy image compression plays a crucial role in the modern digital world by balancing image quality and file size. By selectively discarding nonessential image data, lossy compression enables efficient storage, transmission, and sharing of images, making it indispensable in various applications, from web content to multimedia messaging.