Compression Compression algorithms reduce the number of bytes required to represent data and the amount of memory required to store images. Compression allows a larger number of images to be stored on a given medium and increases the amount of data that can be sent over the internet.
Full Answer
What is the best type of image compression?
Best Free Image Optimization Tools for Image Compression. 1. JPEG Optimizer. JPEG Optimizer allows you to upload and compress your photos online. This simple tool works, as its name implies, only on JPEG files. What’s great about JPEG Optimizer is that it allows you to resize your images before you optimize.
What is the best lossless compression algorithm?
- The Calgary Corpus dating back to 1987 is no longer widely used due to its small size. ...
- The Large Text Compression Benchmark and the similar Hutter Prize both use a trimmed Wikipedia XML UTF-8 data set.
- The Generic Compression Benchmark, maintained by Matt Mahoney, tests compression of data generated by random Turing machines.
How to write a compression algorithm?
- Create a leaf node for each unique character and build a min heap of all leaf nodes (Min Heap is used as a priority queue. ...
- Extract two nodes with the minimum frequency from the min heap.
- Create a new internal node with a frequency equal to the sum of the two nodes frequencies. ...
- Repeat steps#2 and #3 until the heap contains only one node. ...
What are some common compression algorithms?
Raster graphics
- AVIF – AOMedia Video 1 Image File Format
- FLIF – Free Lossless Image Format
- HEIF – High Efficiency Image File Format (lossless or lossy compression, using HEVC)
- ILBM – (lossless RLE compression of Amiga IFF images)
- JBIG2 – (lossless or lossy compression of B&W images)
What is JPEG?
JPEG stands for Joint Photographic Experts Group, which was a group of image processing experts that devised a standard for compressing images (ISO).
The underlying assumptions of the JPEG algorithm
The JPEG algorithm is designed specifically for the human eye. It exploits the following biological properties of human sight:
An uncompressed image of mixed peppers
MATLAB has various images uploaded into MATLAB. Here, is one of some peppers:
Color transform: Convert RGB to YCbCr
Right now, the image is stored in RGB format. While this colorspace is convenient for projecting the image on the computer screen, it does not isolate the illuminance and color of an image. The intensity of color is intermixed in the colorspace.
Our eyes are senstitive to illuminance, but not color
Since our eyes are not particularly sensitive to chrominance, we can "downsample" that stuff. Here, we remove x100 amount of "color" from the image and see that it has barely changed:
Eyes are senstitive to intensity
However, if we downsample the illuminance by x10, then there is a noticeable difference. (You'll have to zoom in to see it.)
JPEG downsampling
Here, we will be a little conservative and downsample the chrominance by only a factor of 2.
What is compressed data?
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.
What is lossy compression?
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.
What is DCT in JPEG?
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. Reducing the color space to the most common colors in the image.
When was JPEG 2000 created?
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.
When did JPEG become a file format?
DCT compression became the basis for JPEG, which 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.
When was the deflate algorithm introduced?
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. Wavelet coding, the use of wavelet transforms in image compression, began after the development of DCT coding.
What is resolution progressive?
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.
Image Compression: ML Techniques and Applications
In this article, we will discuss Image Compression application in depth involving Machine Learning Techniques like RNN based Encoder and Decoder and applications of Image Compression.
Introduction
In recent years, the development and demand for multimedia products has accelerated, resulting in network bandwidth and memory device storage shortages. As a result, image compression theory is becoming increasingly important for minimising data redundancy and saving more hardware space and transmission bandwidth.
Image Compression
Image compression is a type of data compression in which the original image is encoded with a small number of bits. The goal of picture compression is to eliminate image redundancy and store or transfer data in a more efficient manner. The block diagram of the generic image storage system is shown in Figure 1.1.
Need of Image Compression
The raw data for single coloured photos does not require a lot of storage space. The overall storage needs become higher as the quantity of photos to be kept or images to be delivered grows.
Compression Techniques and Algorithm
Compression is a procedure where particular information is characterized with reduced number of data points.
Lossless Compression
This method is commonly used for archive purposes. Lossless compression is suggested for images with geometric forms that are relatively basic. It's used for medical, technical, and clip art graphics, among other things.
Lossy Compression
Lossy compression algorithms are very useful for compressing natural pictures such as photographs, where a small loss in reliability is sufficient to achieve a significant decrease in bit rate. This is the most common method for compressing multimedia data, and some data may be lost as a result.
Why is compression algorithm used?
Compression algorithms are normally used to reduce the size of a file without removing information. This can increase their entropy and make the files appear more random because all of the possible bytes become more common.
How does compression affect data?
Compression algorithms reduce the number of bytes required to represent data and the amount of memory required to store images. Compression allows a larger number of images to be stored on a given medium and increases the amount of data that can be sent over the internet.
What is the difference between JPEG and MPEG?
Each JPEG image is a new fresh image. This is very useful where the frame rate must be very low, such as on an offshore oil platform with a very low-bandwidth satellite uplink, or where only a dial-up modem connection is available for network connectivity. I used JPEG on an offshore platform with only a 64 kb/s satellite connection available. MPEG is most useful where there is adequate data bandwidth available for a fast-moving image but where it is desirable to conserve network resources for future growth and for network stability.
What is BDI compression?
The BDI compression algorithm is naturally amenable toward implementation using assist warps because of its data-parallel nature and simplicity. The CABA framework can also be used to realize other algorithms. The challenge in implementing algorithms like FPC [59] and C-Pack [ 17 ], which have variable-length compressed words, is primarily in the placement of compressed words within the compressed cache lines. In BDI, the compressed words are in fixed locations within the cache line, and for each encoding, all the compressed words are of the same size and can, therefore, be processed in parallel. In contrast, C-Pack may employ multiple dictionary values as opposed to just one base in BDI. In order to realize algorithms with variable length words and dictionary values with assist warps, we leverage the coalescing/address generation logic [ 60, 61] already available in the GPU cores. We make two minor modifications to these algorithms [ 17, 59] to adapt them for use with CABA. First, similar to prior works [ 17, 54, 59 ], we observe that few encodings are sufficient to capture almost all the data redundancy. In addition, the impact of any loss in compressibility because of fewer encodings is minimal as the benefits of bandwidth compression are at multiples of a only single DRAM burst (e.g., 32B for GDDR5 [ 62 ]). We exploit this to reduce the number of supported encodings. Second, we place all the metadata containing the compression encoding at the head of the cache line to be able to determine how to decompress the entire line upfront. In the case of C-Pack, we place the dictionary entries after the metadata.
What is dictionary compression?
Dictionary compression algorithms use no statistical models. They focus on the memory on the strings already seen. The memory may be an explicit dictionary that can be extended infinitely, or an implicit limited dictionary as sliding windows. Each seen string is stored into a dictionary with an index.
Where is the compressor located in a communication channel?
A compression algorithm is often called compressor and the decompression algorithm is called decompressor. The compressor and decompressor can be located at two ends of a communication channel, at the source and at the destination respectively.
Is motion compensation encoded as a whole?
To make the process of comparison more efficient, a frame is not encoded as a whole. Rather, it is split into blocks, and the blocks are encoded and then compared. Motion compensation is a central part of MPEG-2 (as well as MPEG-4) standards. It is the most demanding of the computational algorithms of a video encoder.
Why Do We Need Image Compression?
Consider a black and white image that has a resolution of 1000*1000 and each pixel uses 8 bits to represent the intensity. So the total no of bits req= 1000*1000*8 = 80,00,000 bits per image.
Transforming The Image
It a function that maps from one domain (vector space) to another domain (other vector space). Assume, T is a transform, f (t):X->X’ is a function then, T (f (t)) is called the transform of the function.
Transforms in Image Processing
The image is also a function of the location of the pixels. i.e I (x, y) where (x, y) are the coordinates of the pixel in the image. So we generally transform an image from the spatial domain to the frequency domain.
Quantization
The process quantization is a vital step in which the various levels of intensity are grouped into a particular level based on the mathematical function defined on the pixels.
Symbol Encoding
The symbol stage involves where the distinct characters involved in the image are encoded in a way that the no. of bits required to represent a character is optimal based on the frequency of the character’s occurrence. In simple terms, In this stage codewords are generated for the different characters present. By doing so we aim to reduce the no.
What is data compression?
Data compression is the process of reducing file sizes while retaining the same or a comparable approximation of data. This is accomplished by eliminating unnecessary data or by reformatting data for greater efficiency.
What is lossless compression?
Lossless compression algorithms are typically used for archival or other high fidelity purposes. These algorithms enable you to reduce file size while ensuring that files can be fully restored to their original state if need be.
What is the LZ77 algorithm?
Below are six commonly used ones. 1. LZ77. LZ77, released in 1977, is the base of many other lossless compression algorithms. It uses a “sliding window” method. In this method, LZ77 manages a dictionary that uses triples to represent: Offset—the distance between the start of a phrase and the beginning of a file.
What is deepcoder video?
DeepCoder is a Convolutional Neural Network (CNN) based framework, which presents an alternative to traditional video compression techniques. The model uses separate Convolutional Neural Networks (CNNs) for predictive and residual signals. It encodes feature maps into a binary stream using scalar quantization and a traditional file compression algorithm, Huffman encoding. The model is said to provide superior performance compared to the well-known H.264/AVC video coding standard. See the full paper.
What is the deflation algorithm?
Huffman coding is an algorithm developed in 1952. It is an entropy encoding method that assigns codes based on the frequency of a character.
What is a Gan?
GANs are a form of neural network that use two networks in competition to produce more accurate analyses and predictions. GAN-based compression algorithms were first developed in 2017. These algorithms can compress files up to two and a half times smaller than other commonly used methods, such as JPEG or WebP.
What is LZMA in a file?
Lempel-Ziv Markov chain Algorithm (LZMA), released in 1998, is a modification of LZ77 designed for the 7-Zip archiver with a .7z format. It uses a chain compression method that applies the modified LZ77 algorithm at a bit rather than byte level. The output from this compression is then processed using arithmetic coding for further compression. Depending on the exact implementation, other compression steps may also be performed.
What Is Image Compression?
Image compression involves reducing the pixels, dimensions or color components of images in order to reduce their file size. This reduces their storage and processing burden (for web performance). Advanced image optimization techniques can identify the more important image components and discard less crucial components.
Image Compression with Deep Learning
Deep Learning (DL) has been u sed for image compression since the 1980s, and has evolved to include techniques such as multi-layer perceptrons, random neural networks, convolutional neural networks and generative adversarial networks.
Frameworks and Libraries for AI-Based Image Compression
It is theoretically possible to code an entire image processing application yourself, but it is more realistic to leverage what others have developed, and simply adjust or extend existing software according to your own needs. Many existing frameworks and libraries provide models for image processing, many of them pre-trained on large data sets.
Conclusion
In this article I discussed the state of the art in image compression algorithms based on deep learning, including Multi-Layer Perceptrons, Convolutional Neural Networks, and Generative Adversarial Networks. I also presented readily available tools you can use to build AI-based image compression applications:
LZSS
LZSS stands for Lempel Ziv Storer Szymanski and it was developed and announced in the year 1982. This is a data compression algorithm that improves on LZ77. This process of compression is done by including a method that will keep an eye on whether a substitution decreases the file size.
Deflate
Deflate is a lossless data compression algorithm file format that makes the use of a combination of LZSS and Huffman coding. It was designed by Phil Katz in the year 1993. Huffman coding is also an algorithm that was developed in the year 1952.
LZMA
LZMA stands for the Lempel Ziv Markov chain algorithm and it was designed and released in the year 1998. It can be also said as a modification of the LZ77. This modification was done for the –Zip archiver with a .7z format. The method of chain compression is being used that implements the modified LZ77 at a bit rather than byte level.
LZMA2
LZMA2 was designed and released in the year 2009. It can also be defined as the modification of LZMA. It makes the LZMA better by improving its performance with greater multithreading capabilities. LZMA2 also provides you with improved handling of incompressible data.
Multi-Layer Perceptron (MLP)- Based Compression
MLP can be defined as a technology that uses multiple neuron layers for input, processing, and giving output data. It can be implemented for the reduction of dimension tasks and also compression of data. The MLP-based algorithm was first developed in the year 1988 and join the already existing processes of:
Dee Coder- Deep Neural Network Based Video Compression
Deep Coder is defined as a Convolutional Neural Network (CNN) based framework. This makes the representation of an alternative to those video compression techniques that we have been using so long. For predictive and residual signals different Convolutional Neural Networks (CNNs) are bring used by this model.
Convolutional Neural Network (CNN) – Based compression
CNN’s are defined as neural networks of different layers. This is majorly used for the recognition of images and detection of the feature. The moment you apply it to compression, these networks make use of convolution for calculating the connection between neighboring pixels.
Overview
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 re…
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 c…
History
Entropy coding started in the 1940s with the introduction of Shannon–Fano coding, the basis for Huffman coding which was developed in 1950. 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 …
External links
• Image compression – lecture from MIT OpenCourseWare
• Image Coding Fundamentals
• A study about image compression – with basics, comparing different compression methods like JPEG2000, JPEG and JPEG XR / HD Photo