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Image Compression and Coding - Fundamentals of visual data compression, Redundancy, models, Error-free compression, Variable Length Coding (VLC)

techniques based pixel values

Oge Marques
Department of Computer Science and Engineering
Florida Atlantic University, Boca Raton, FL, USA

Definition: Image compression deals with reducing the amount of data required to represent a digital image by removing of redundant data.

Images can be represented in digital format in many ways. Encoding the contents of a 2-D image in a raw bitmap (raster) format is usually not economical and may result in very large files. Since raw image representations usually require a large amount of storage space (and proportionally long transmission times in the case of file uploads/ downloads), most image file formats employ some type of compression. The need to save storage space and shorten transmission time, as well as the human visual system tolerance to a modest amount of loss, have been the driving factors behind image compression techniques.

Compression methods can be lossy, when a tolerable degree of deterioration in the visual quality of the resulting image is acceptable, or lossless, when the image is encoded in its full quality. The overall results of the compression process, both in terms of storage savings – usually expressed numerically in terms of compression ratio (CR) or bits per pixel (bpp) – as well as resulting quality loss (for the case of lossy techniques) may vary depending on the technique, format, options (such as the quality setting for JPEG), and the image contents. As a general guideline, lossy compression should be used for general purpose photographic images, whereas lossless compression should be preferred when dealing with line art, technical drawings, cartoons, etc. or images in which no loss of detail may be tolerable (most notably, space images and medical images).

We will review the most important concepts behind image compression and coding techniques and survey some of the most popular algorithms and standards.

Fundamentals of visual data compression

The general problem of image compression is to reduce the amount of data required to represent a digital image or video and the underlying basis of the reduction process is the removal of redundant data. Mathematically, visual data compression typically involves transforming (encoding) a 2-D pixel array into a statistically uncorrelated data set. This transformation is applied prior to storage or transmission. At some later time, the compressed image is decompressed to reconstruct the original image information (preserving or lossless techniques) or an approximation of it (lossy techniques).


Data compression is the process of reducing the amount of data required to represent a given quantity of information. Different amounts of data might be used to communicate the same amount of information. If the same information can be represented using different amounts of data, it is reasonable to believe that the representation that requires more data contains what is technically called data redundancy.

Image compression and coding techniques explore three types of redundancies: coding redundancy, interpixel (spatial) redundancy, and psychovisual redundancy. The way each of them is explored is briefly described below.

  • Coding redundancy: consists in using variable-length codewords selected as to match the statistics of the original source, in this case, the image itself or a processed version of its pixel values. This type of coding is always reversible and usually implemented using look-up tables (LUTs). Examples of image coding schemes that explore coding redundancy are the Huffman codes and the arithmetic coding technique.
  • Interpixel redundancy: this type of redundancy – sometimes called spatial redundancy, interframe redundancy, or geometric redundancy – exploits the fact that an image very often contains strongly correlated pixels, in other words, large regions whose pixel values are the same or almost the same. This redundancy can be explored in several ways, one of which is by predicting a pixel value based on the values of its neighboring pixels. In order to do so, the original 2-D array of pixels is usually mapped into a different format, e.g., an array of differences between adjacent pixels. If the original image pixels can be reconstructed from the transformed data set the mapping is said to be reversible. Examples of compression techniques that explore the interpixel redundancy include: Constant Area Coding (CAC), (1-D or 2-D) Run-Length Encoding (RLE) techniques, and many predictive coding algorithms such as Differential Pulse Code Modulation (DPCM).
  • Psychovisual redundancy: many experiments on the psychophysical aspects of human vision have proven that the human eye does not respond with equal sensitivity to all incoming visual information; some pieces of information are more important than others. The knowledge of which particular types of information are more or less relevant to the final human user have led to image and video compression techniques that aim at eliminating or reducing any amount of data that is psychovisually redundant. The end result of applying these techniques is a compressed image file, whose size and quality are smaller than the original information, but whose resulting quality is still acceptable for the application at hand. The loss of quality that ensues as a byproduct of such techniques is frequently called quantization, as to indicate that a wider range of input values is normally mapped into a narrower range of output values thorough an irreversible process. In order to establish the nature and extent of information loss, different fidelity criteria (some objective such as root mean square (RMS) error, some subjective, such as pairwise comparison of two images encoded with different quality settings) can be used. Most of the image coding algorithms in use today exploit this type of redundancy, such as the Discrete Cosine Transform (DCT)-based algorithm at the heart of the JPEG encoding standard.

Image compression and coding models

Figure 1 shows a general image compression model. It consists of a source encoder, a channel encoder, the storage or transmission media (also referred to as channel ), a channel decoder, and a source decoder. The source encoder reduces or eliminates any redundancies in the input image, which usually leads to bit savings. Source encoding techniques are the primary focus of this discussion. The channel encoder increase noise immunity of source encoder’s output, usually adding extra bits to achieve its goals. If the channel is noise-free, the channel encoder and decoder may be omitted. At the receiver’s side, the channel and source decoder perform the opposite functions and ultimately recover (an approximation of) the original image.

Figure 2 shows the source encoder in further detail. Its main components are:

  • Mapper: transforms the input data into a (usually nonvisual) format designed to reduce interpixel redundancies in the input image. This operation is generally reversible and may or may not directly reduce the amount of data required to represent the image.
  • Quantizer: reduces the accuracy of the mapper’s output in accordance with some pre-established fidelity criterion. Reduces the psychovisual redundancies of the input image. This operation is not reversible and must be omitted if lossless compression is desired.
  • Symbol (entropy) encoder: creates a fixed- or variable-length code to represent the quantizer’s output and maps the output in accordance with the code. In most cases, a variable-length code is used. This operation is reversible.

Error-free compression

Error-free compression techniques usually rely on entropy-based encoding algorithms. The concept of entropy is mathematically described in equation (1):


a j is a symbol produced by the information source

P ( a j ) is the probability of that symbol

J is the total number of different symbols

H ( z ) is the entropy of the source.

The concept of entropy provides an upper bound on how much compression can be achieved, given the probability distribution of the source. In other words, it establishes a theoretical limit on the amount of lossless compression that can be achieved using entropy encoding techniques alone.

Variable Length Coding (VLC)

Most entropy-based encoding techniques rely on assigning variable-length codewords to each symbol, whereas the most likely symbols are assigned shorter codewords. In the case of image coding, the symbols may be raw pixel values or the numerical values obtained at the output of the mapper stage (e.g., differences between consecutive pixels, run-lengths, etc.). The most popular entropy-based encoding technique is the Huffman code. It provides the least amount of information units (bits) per source symbol. It is described in more detail in a separate short article.

Run-length encoding (RLE)

RLE is one of the simplest data compression techniques. It consists of replacing a sequence (run) of identical symbols by a pair containing the symbol and the run length. It is used as the primary compression technique in the 1-D CCITT Group 3 fax standard and in conjunction with other techniques in the JPEG image compression standard (described in a separate short article).

Differential coding

Differential coding techniques explore the interpixel redundancy in digital images. The basic idea consists of applying a simple difference operator to neighboring pixels to calculate a difference image, whose values are likely to follow within a much narrower range than the original gray-level range. As a consequence of this narrower distribution – and consequently reduced entropy – Huffman coding or other VLC schemes will produce shorter codewords for the difference image.

Predictive coding

Predictive coding techniques constitute another example of exploration of interpixel redundancy, in which the basic idea is to encode only the new information in each pixel. This new information is usually defined as the difference between the actual and the predicted value of that pixel.

Figure 3 shows the main blocks of a lossless predictive encoder. The key component is the predictor, whose function is to generate an estimated (predicted) value for each pixel from the input image based on previous pixel values. The predictor’s output is rounded to the nearest integer and compared with the actual pixel value: the difference between the two – called prediction error – is then encoded by a VLC encoder. Since prediction errors are likely to be smaller than the original pixel values, the VLC encoder will likely generate shorter codewords.

There are several local, global, and adaptive prediction algorithms in the literature. In most cases, the predicted pixel value is a linear combination of previous pixels.

Dictionary-based coding

Dictionary-based coding techniques are based on the idea of incrementally building a dictionary (table) while receiving the data. Unlike VLC techniques, dictionary-based techniques use fixed-length codewords to represent variable-length strings of symbols that commonly occur together. Consequently, there is no need to calculate, store, or transmit the probability distribution of the source, which makes these algorithms extremely convenient and popular. The best-known variant of dictionary-based coding algorithms is the LZW (Lempel-Ziv-Welch) encoding scheme, used in popular multimedia file formats such as GIF, TIFF, and PDF.

Lossy compression

Lossy compression techniques deliberately introduce a certain amount of distortion to the encoded image, exploring the psychovisual redundancies of the original image. These techniques must find an appropriate balance between the amount of error (loss) and the resulting bit savings.


The quantization stage is at the core of any lossy image encoding algorithm. Quantization, in at the encoder side, means partitioning of the input data range into a smaller set of values. There are two main types of quantizers: scalar quantizers and vector quantizers. A scalar quantizer partitions the domain of input values into a smaller number of intervals. If the output intervals are equally spaced, which is the simplest way to do it, the process is called uniform scalar quantization; otherwise, for reasons usually related to minimization of total distortion, it is called nonuniform scalar quantization. One of the most popular nonuniform quantizers is the Lloyd-Max quantizer. Vector quantization (VQ) techniques extend the basic principles of scalar quantization to multiple dimensions. Because of its fast lookup capabilities at the decoder side, VQ-based coding schemes are particularly attractive to multimedia applications.

Transform coding

The techniques discussed so far work directly on the pixel values and are usually called spatial domain techniques. Transform coding techniques use a reversible, linear mathematical transform to map the pixel values onto a set of coefficients, which are then quantized and encoded. The key factor behind the success of transform-based coding schemes many of the resulting coefficients for most natural images have small magnitudes and can be quantized (or discarded altogether) without causing significant distortion in the decoded image. Different mathematical transforms, such as Fourier (DFT), Walsh-Hadamard (WHT), and Karhunen-Loeve (KLT), have been considered for the task. For compression purposes, the higher the capability of compressing information in fewer coefficients, the better the transform; for that reason, the Discrete Cosine Transform (DCT) has become the most widely used transform coding technique.

Transform coding algorithms (Figure 4) usually start by partitioning the original image into subimages (blocks) of small size (usually 8 × 8). For each block the transform coefficients are calculated, effectively converting the original 8 × 8 array of pixel values into an array of coefficients within which the coefficients closer to the top-left corner usually contain most of the information needed to quantize and encode (and eventually perform the reverse process at the decoder’s side) the image with little perceptual distortion. The resulting coefficients are then quantized and the output of the quantizer is used by a (combination of) symbol encoding technique(s) to produce the output bitstream representing the encoded image. At the decoder’s side, the reverse process takes place, with the obvious difference that the ‘dequantization’ stage will only generate an approximated version of the original coefficient values; in other words, whatever loss was introduced by the quantizer in the encoder stage is not reversible.

Wavelet coding

Wavelet coding techniques are also based on the idea that the coefficients of a transform that decorrelates the pixels of an image can be coded more efficiently than the original pixels themselves. The main difference between wavelet coding and DCT-based coding (Figure 4) is the omission of the first stage. Because wavelet transforms are capable of representing an input signal with multiple levels of resolution, and yet maintain the useful compaction properties of the DCT, the subdivision of the input image into smaller subimages is no longer necessary. Wavelet coding has been at the core of the latest image compression standards, most notably JPEG 2000, which is discussed in a separate short article.

Image compression standards

Work on international standards for image compression started in the late 1970s with the CCITT (currently ITU-T) need to standardize binary image compression algorithms for Group 3 facsimile communications. Since then, many other committees and standards have been formed to produce de jure standards (such as JPEG), while several commercially successful initiatives have effectively become de facto standards (such as GIF). Image compression standards bring about many benefits, such as: (1) easier exchange of image files between different devices and applications; (2) reuse of existing hardware and software for a wider array of products; (3) existence of benchmarks and reference data sets for new and alternative developments.

Binary image compression standards

Work on binary image compression standards was initially motivated by CCITT Group 3 and 4 facsimile standards. The Group 3 standard uses a non-adaptive, 1-D RLE technique in which the last K-1 lines of each group of K lines (for K = 2 or 4) are optionally coded in a 2-D manner, using the Modified Relative Element Address Designate (MREAD) algorithm. The Group 4 standard uses only the MREAD coding algorithm. Both classes of algorithms are non-adaptive and were optimized for a set of eight test images, containing a mix of representative documents, which sometimes resulted in data expansion when applied to different types of documents (e.g., half-tone images).. The Joint Bilevel Image Group (JBIG)– a joint committee of the ITU-T and ISO – has addressed these limitations and proposed two new standards (JBIG and JBIG2) which can be used to compress binary and gray-scale images of up to 6 gray-coded bits/pixel.

Continuous tone still image compression standards

For photograph quality images (both grayscale and color), different standards have been proposed, mostly based on lossy compression techniques. The most popular standard in this category, by far, is the JPEG standard, a lossy, DCT-based coding algorithm. Despite its great popularity and adoption, ranging from digital cameras to the World Wide Web, certain limitations of the original JPEG algorithm have motivated the recent development of two alternative standards, JPEG 2000 and JPEG-LS (lossless). JPEG, JPEG 2000, and JPEG-LS are described in separate short articles.

Image Data Representations - Binary (1-bit) images, Gray-level (8-bit) images [next] [back] Image and Video Quality Assessment - Introduction, Why Do We Need Quality Assessment?, Why is Quality Assessment So Hard?

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almost 5 years ago

Please send me the Pdf Notes on Digital image processing.

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over 1 year ago

This material is very crisp and excellent.
Thanks. Can I have material on various lossy and loseless encoding techniques?

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almost 5 years ago

Thank you!!

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almost 5 years ago

hello sir/mam

please send me matlab code for "image quality assessment techniques pn spatial domain"..

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almost 5 years ago

hello sir/mam

please send me matlab code for "image quality assessment techniques pn spatial domain"..

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over 5 years ago

provides clear cut information..thanks for sharing knowledge///

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over 5 years ago

thanks very much, this helped me to understand the topic very well.i was confused what to do. thanks again

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over 5 years ago

Image Compression and Coding - Fundamentals of visual data compression, Redundancy, models, Error-free compression, Variable Length Coding (VLC)

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over 5 years ago

thanks for this I needed to get compressed my sites images because they are too heavy. thank you so much

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over 6 years ago

this is very remarkable......
very good explation about data redundancy...