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- Is there a faster lossy compression than JPEG? - Stack Overflow
- Image Compression tools via command line - Stack Overflow
- How to compress of reduce the size of an image before uploading …
- How can I compress images using java? - Stack Overflow
- Compress image using sharp in node.js - Stack Overflow
- jpeg compression ratio - Stack Overflow
- Best way to compress an image Javascript React Web App
- How to compress an image via Javascript in the browser?
- How to do on the fly image compression in C#? - Stack Overflow
- huffman code - Matlab, Image compression - Stack Overflow
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Image compression is a type of data compression applied to digital images, to reduce their cost for storage or transmission. Algorithms may take advantage of visual perception and the statistical properties of image data to provide superior results compared with generic data compression methods which are used for other digital data.
Lossy and lossless image compression
Image compression may be lossy or lossless. Lossless compression is preferred for archival purposes and often for medical imaging, technical drawings, clip art, or comics. Lossy compression methods, especially when used at low bit rates, introduce compression artifacts. Lossy methods are especially suitable for natural images such as photographs in applications where minor (sometimes imperceptible) loss of fidelity is acceptable to achieve a substantial reduction in bit rate. Lossy compression that produces negligible differences may be called visually lossless.
Methods for lossy compression:
Transform coding – This is the most commonly used method.
Discrete Cosine Transform (DCT) – The most widely used form of lossy compression. It is a type of Fourier-related transform, and was originally developed by Nasir Ahmed, T. Natarajan and K. R. Rao in 1974. The DCT is sometimes referred to as "DCT-II" in the context of a family of discrete cosine transforms (see discrete cosine transform). It is generally the most efficient form of image compression.
DCT is used in JPEG, the most popular lossy format, and the more recent HEIF.
The more recently developed wavelet transform is also used extensively, followed by quantization and entropy coding.
Color quantization - Reducing the color space to a few "representative" colors in the image. The selected colors are specified in the color palette in the header of the compressed image. Each pixel just references the index of a color in the color palette. This method can be combined with dithering to avoid posterization.
Whole-image palette, typically 256 colors, used in GIF and PNG file formats.
block palette, typically 2 or 4 colors for each block of 4x4 pixels, used in BTC, CCC, S2TC, and S3TC.
Chroma subsampling. This takes advantage of the fact that the human eye perceives spatial changes of brightness more sharply than those of color, by averaging or dropping some of the chrominance information in the image.
Fractal compression.
More recently, methods based on Machine Learning were applied, using Multilayer perceptrons, Convolutional neural networks, Generative adversarial networks and Diffusion models. Implementations are available in OpenCV, TensorFlow, MATLAB's Image Processing Toolbox (IPT), and the High-Fidelity Generative Image Compression (HiFiC) open source project.
Methods for lossless compression:
Run-length encoding – used in default method in PCX and as one of possible in BMP, TGA, TIFF
Predictive coding – used in DPCM
Entropy encoding – the two most common entropy encoding techniques are arithmetic coding and Huffman coding
Adaptive dictionary algorithms such as LZW – used in GIF and TIFF
DEFLATE – used in PNG, MNG, and TIFF
Chain codes
Other properties
The best image quality at a given compression rate (or bit rate) is the main goal of image compression, however, there are other important properties of image compression schemes:
Scalability generally refers to a quality reduction achieved by manipulation of the bitstream or file (without decompression and re-compression). Other names for scalability are progressive coding or embedded bitstreams. Despite its contrary nature, scalability also may be found in lossless codecs, usually in form of coarse-to-fine pixel scans. Scalability is especially useful for previewing images while downloading them (e.g., in a web browser) or for providing variable quality access to e.g., databases. There are several types of scalability:
Quality progressive or layer progressive: The bitstream successively refines the reconstructed image.
Resolution progressive: First encode a lower image resolution; then encode the difference to higher resolutions.
Component progressive: First encode grey-scale version; then adding full color.
Region of interest coding. Certain parts of the image are encoded with higher quality than others. This may be combined with scalability (encode these parts first, others later).
Meta information. Compressed data may contain information about the image which may be used to categorize, search, or browse images. Such information may include color and texture statistics, small preview images, and author or copyright information.
Processing power. Compression algorithms require different amounts of processing power to encode and decode. Some high compression algorithms require high processing power.
The quality of a compression method often is measured by the peak signal-to-noise ratio. It measures the amount of noise introduced through a lossy compression of the image, however, the subjective judgment of the viewer also is regarded as an important measure, perhaps, being the most important measure.
History
Entropy coding started in the late 1940s with the introduction of Shannon–Fano coding, the basis for Huffman coding which was published in 1952. Transform coding dates back to the late 1960s, with the introduction of fast Fourier transform (FFT) coding in 1968 and the Hadamard transform in 1969.
An important development in image data compression was the discrete cosine transform (DCT), a lossy compression technique first proposed by Nasir Ahmed, T. Natarajan and K. R. Rao in 1973. JPEG was introduced by the Joint Photographic Experts Group (JPEG) in 1992. JPEG compresses images down to much smaller file sizes, and has become the most widely used image file format. JPEG was largely responsible for the wide proliferation of digital images and digital photos, with several billion JPEG images produced every day as of 2015.
Lempel–Ziv–Welch (LZW) is a lossless compression algorithm developed by Abraham Lempel, Jacob Ziv and Terry Welch in 1984. It is used in the GIF format, introduced in 1987. DEFLATE, a lossless compression algorithm developed by Phil Katz and specified in 1996, is used in the Portable Network Graphics (PNG) format.
The JPEG 2000 standard was developed from 1997 to 2000 by a JPEG committee chaired by Touradj Ebrahimi (later the JPEG president). In contrast to the DCT algorithm used by the original JPEG format, JPEG 2000 instead uses discrete wavelet transform (DWT) algorithms. It uses the CDF 9/7 wavelet transform (developed by Ingrid Daubechies in 1992) for its lossy compression algorithm, and the Le Gall–Tabatabai (LGT) 5/3 wavelet transform (developed by Didier Le Gall and Ali J. Tabatabai in 1988) for its lossless compression algorithm. JPEG 2000 technology, which includes the Motion JPEG 2000 extension, was selected as the video coding standard for digital cinema in 2004.
The evolution of image compression technologies has led to continuous improvements in both efficiency and quality. From the early developments in entropy coding and transform coding to the introduction of JPEG and JPEG 2000, these innovations have significantly impacted the way digital images are stored, transmitted, and processed. Modern compression methods allow users to optimize image files for faster loading times and better storage utilization, while maintaining high image quality. As compression technologies advance, these methods continue to play a crucial role in various fields, including web development, digital media, and content management.
Huffman Coding
Huffman coding is a fundamental technique used in image compression algorithms to achieve efficient data representation. Named after its inventor David A. Huffman, this method is widely employed in various image compression standards such as JPEG and PNG.
= Principle of Huffman Coding
=Huffman coding is a form of entropy encoding that assigns variable-length codes to input symbols based on their frequencies of occurrence. The basic principle is to assign shorter codes to more frequently occurring symbols and longer codes to less frequent symbols, thereby reducing the average code length compared to fixed-length codes.
= Application in Image Compression
=In image compression, Huffman coding is typically applied after other transformations like Discrete Cosine Transform (DCT) in the case of JPEG compression. After transforming the image data into a frequency domain representation, Huffman coding is used to encode the transformed coefficients efficiently.
= Steps in Huffman Coding for Image Compression
=Frequency Analysis: Calculate the frequency of occurrence of each symbol or symbol combination in the transformed image data.
Constructing the Huffman Tree: Build a Huffman tree based on the symbol frequencies. The tree is constructed recursively by combining the nodes with the lowest frequencies until a single root node is formed.
Assigning Codewords: Traverse the Huffman tree to assign variable-length codewords to each symbol, with shorter codewords assigned to more frequent symbols.
Encoding: Replace the original symbols in the image data with their corresponding Huffman codewords to generate the compressed data stream.
= Benefits of Huffman Coding in Image Compression
=Lossless Compression: Huffman coding can be used in both lossy and lossless image compression techniques, providing flexibility in balancing between compression ratio and image quality.
Efficiency: By assigning shorter codes to frequently occurring symbols, Huffman coding reduces the average code length, resulting in efficient data representation and reduced storage requirements.
Compatibility: Huffman coding is widely supported and can be seamlessly integrated into existing image compression standards and algorithms.
= Conclusion
=Huffman coding plays a crucial role in image compression by efficiently encoding image data into a compact representation. Its ability to adaptively assign variable-length codewords based on symbol frequencies makes it an essential component in modern image compression techniques, contributing to the reduction of storage space and transmission bandwidth while maintaining image quality.
Notes and references
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Daftar Isi
Is there a faster lossy compression than JPEG? - Stack Overflow
Dec 29, 2010 · DCT-compressed images can be put in a TIFF container, so technically TIFF can be either lossy or non-. Doesn't change the baseline observation that DCT is just about the only game in town for lossy image compression, though. –
Image Compression tools via command line - Stack Overflow
Oct 3, 2013 · I'm using the following tools to perform lossless image compression: pngcrush; gifsicle; jpegtran; For each of the programs, I've created two shortcuts: One that does the actual compression, and shows the file size of both files
How to compress of reduce the size of an image before uploading …
Apr 19, 2015 · This is a really bad idea, it is full of terrible edge cases. 1) It starts with compressingValue of 1.0 which means hardly any compression. If the image dimensions are small, then the images will end up being many more KB than they need to. 2) If the images are large, it will be slow as it may recompress many times to get under the target size.
How can I compress images using java? - Stack Overflow
This is only for one image but I am sure I will have lots of images for compression. so how can I compress image by passing bytes or percentage as a parameter or using anyother calculations in java (by using API or image-processing Tool) so,that I can get compressed version of image as suggested by google. Thanks in advance.
Compress image using sharp in node.js - Stack Overflow
I want to resize and compress images using sharp in node.js In sharp for jpeg there is separate compression and for webp there is separate and for png there is separate. WEBP sharp('a.jpg') .res...
jpeg compression ratio - Stack Overflow
Is there a table that gives the compression ratio of a jpeg image at a given quality? Something like the table given on the wiki page, except for more values. A formula could also do the trick. Bonus: Are the [compression ratio] values on the wiki page roughly true for all images? Does the ratio depend on what the image is and the size of the ...
Best way to compress an image Javascript React Web App
you can use react-image-file-resizer library to compress image . import Resizer from 'react-image-file-resizer'; Resizer.imageFileResizer( file, //is the file of the new image that can now be uploaded...
How to compress an image via Javascript in the browser?
import imageCompression from 'browser-image-compression'; const options = { maxSizeMB: 0.5, // pretty much self-explanatory maxWidthOrHeight: 500, // apparently px } imageCompression(file, options) .then(function(compressedFile) { console.log( "compressedFile instanceof Blob", compressedFile instanceof Blob ); // true console.log ...
How to do on the fly image compression in C#? - Stack Overflow
Jul 9, 2014 · Indeed there are many image processing libraries - look it up... Also try starting small - write code that resizes image on the fly to some fixed size (already have everything in .Net, unlike compression with different quality) and see if/how it fits into your app.
huffman code - Matlab, Image compression - Stack Overflow
Dec 26, 2011 · This means that the input of the algorithm is an image and the output is a numeric code that is smaller in size than the input: hence the compression. The principle of Huffman coding is (roughly) to replace symbols in the original data (in your case the value of each pixel of the image) by a numeric code that is attributed according to the ...