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How can you use AI to compress images without losing quality?

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Image compression is the process of reducing the size of an image file without compromising its visual quality. It is essential for saving storage space, bandwidth, and loading time, especially for web and mobile applications. However, conventional compression methods often introduce artifacts, distortions, or blurriness, especially at high compression ratios. How can you use AI to compress images without losing quality? In this article, you will learn about some of the techniques and benefits of using AI-based image compression and coding.

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Suresh Rao S.

CEO/Founder | Futurist Visionary | Design Strategist | Innovative Product Designer | UX/UI Architect | VR/AR Interface…
MD. NAZMUS SAKIB BIN ALAM

LinkedIn Top Voice for AI | Engineering Talent Recruiter | Executive Vice President | Executive Search | Google…

1 Neural image compression

Neural image compression is a technique that uses deep neural networks to learn how to compress and decompress images efficiently and effectively. Unlike traditional compression methods that rely on fixed rules or standards, neural image compression can adapt to the content and quality of the images, and optimize the trade-off between compression ratio and fidelity. Neural image compression can also leverage the power of generative models, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), to reconstruct images with high perceptual quality and realistic details.

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Carl Holden

?????? 1% ???????????????? ???? ??????????, AI Business Strategist, 15+ Years in Marketing as Agency Owner, Author and Speaker
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This is definitely a viable option. The issue currently is that decompression often results in variability as the full image is being reassembled from compression. If complete accuracy of the original image is the a main goal of the effort like evidence in court or measurements for science, current AI compression models that reduce significant data volume are not a sufficient solution. However, it is likely a solved issue in the future. If the image needs to retain a high visual similarity and it is more aesthetic, these compression models are great!

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2 Image super-resolution

Image super-resolution is a technique that uses AI to enhance the resolution and quality of low-resolution images. It can be used as a post-processing step after compression, or as a standalone technique to improve the appearance of images. Image super-resolution can recover fine details, textures, and edges that are lost or blurred in low-resolution images, and produce sharper and clearer images. Image super-resolution can also benefit from the use of deep learning models, such as convolutional neural networks (CNNs) or GANs, to learn the mapping between low-resolution and high-resolution images, and generate realistic and natural-looking images.

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3 Image coding

Image coding is a technique that uses AI to encode and decode images in a compact and efficient way. It can be seen as a combination of image compression and image super-resolution, where the goal is to reduce the bit rate of the images while maintaining or improving their quality. Image coding can use neural networks to learn the optimal representation and transformation of the images, and exploit the redundancy and correlation among the pixels, regions, or features of the images. Image coding can also use AI to enhance the performance and compatibility of existing image compression standards, such as JPEG or PNG, or create new standards based on AI models.

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4 Benefits of AI-based image compression and coding

AI-based image compression and coding can offer many advantages compared to traditional methods, such as a higher compression ratio, better perceptual quality, and more flexibility and adaptability. AI can achieve greater compression ratios while preserving or improving visual quality, which can save storage space and bandwidth, and reduce the transmission and loading time of the images. Additionally, AI can minimize artifacts and distortions caused by compression, improve resolution, sharpness, and details of the images, thus enhancing user experience and satisfaction. Finally, AI can provide more flexibility by adjusting to the content and quality of the images, as well as optimizing the compression parameters and models according to user preferences. This can enable more customization of the image compression and coding process.

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Tobius Nelson

Gen AI Engineer| Veteran| Aws Solutions Architect
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AI can compress images without sacrificing quality through techniques like lossless compression, which maintains the original resolution and detail. For example, Nero AI Image Compressor is designed to minimize image sizes without degrading visual fidelity. AI tools enhance web performance by using deep learning to reduce image file sizes, improving page load times and SEO. Cloudinary's q_auto feature dynamically optimizes the balance between file size and image quality. Gigapixel AI, favored by professional photographers, can significantly upscale images without quality loss, demonstrating AI's capability to efficiently refine image compression while retaining high precision.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Suresh Rao S.

CEO/Founder | Futurist Visionary | Design Strategist | Innovative Product Designer | UX/UI Architect | VR/AR Interface Specialist | Freelance Expert | Designing Future Solutions
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There are different types, but two popular ones are the Discrete Cosine Transform (DCT) and the Wavelet Transform. They break down the image into smaller pieces, making it easier to store and transmit without losing too much quality. AutoEncoders, the brainy buddies of AI, they decipher the picture's secrets, just like you decode complex ideas. It's like turning confusing design feedback into a clear, straightforward summary, keeping only the important stuff. Simply put, AutoEncoders help images stay sharp, like organizing your ideas and keeping what matters most. Just as you simplify lengthy college notes for better understanding, AI image compression trims down unnecessary details, making visuals concise and clear.

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MD. NAZMUS SAKIB BIN ALAM

LinkedIn Top Voice for AI | Engineering Talent Recruiter | Executive Vice President | Executive Search | Google Certified Data Analyst | Researcher
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There are certain techniques that can be implemented for convenient image processing using AI as a medium. Some of these methods are as follows ??: ? Employing neural network for complex image showcase ? Extracting essential features by discarding irrelevant information ? Using Generative Adversarial Network for high quality image generation from compressed data ? Refining image quality during loading Image processing using AI will be more easier with the emergence of many sophisticated AI based tools ??

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How can you use AI to compress images without losing quality?

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1 Neural image compression

2 Image super-resolution

3 Image coding

4 Benefits of AI-based image compression and coding

5 Here’s what else to consider

Artificial Intelligence

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How can you use AI to compress images without losing quality?

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1 Neural image compression

2 Image super-resolution

3 Image coding

4 Benefits of AI-based image compression and coding

5 Here’s what else to consider

Artificial Intelligence

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