Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications

Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications
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krasnoyarsk.chistka-skvazhin.ru/includes/jamaica/4923.php If we are using the MPEG-4 standard then for the same quality, twice lower is required signal strength. Service information, which contains additional information such as teletext and specific information of network, including an electronic program guide EPG , is generated in digital form and does not require coding. Encoders compress the data by removing irrelevant or redundant parts of the image and sound signals and perform a reduction operation to produce separate video and audio packets of elementary streams.

In , due to the need for storage and playback of moving images and sound in digital format for multimedia applications on various platforms, ISO has formed an expert group called Motion Picture Experts Group MPEG. In order to enable the interconnection of equipment from different manufacturers, standards for compression and transmission of video signals are defined.

Among them, the best known are H. It is aimed to professional digital television [ 8 , 9 ], adopted in , produced on the disadvantages of the standard MPEG It is compatible with MPEG-1 standard, using the same tools and adding some new. Basic innovations with the MPEG-2 standard are as follows: an increased bit rate, picture formats with and without thinning, scalability of quality and time, improved methods of quantization and coding, etc. Since it is primarily designed for TV signal compression, MPEG-2 standard allows the use of both types of image scanning: progressive scanning and scan by line spacing.

In the compression process, all three types of pictures can be coded as I , P and B pictures. Standard encoder structure comprises a mixture of I , P and B frames in a way that I frame appears after every 10—15 frames, and two B frames between two adjacent I frame. Since the complete syntax of the MPEG-2 standard is complex and difficult for practical implementation on a single silicon chip, the MPEG-2 standard defines five subsets of the full syntax, called profiles, which are designed for a variety of applications. These are simple simple profile, main main profile, signal-to-noise ratio SNR scalable profile, spatial scalability spatial scalable and high profile high profile.

Later, another is created, profile, and definition of another multiview profile is in progress. The profile is defined by four levels, which regulate the choice of available parameters during the hardware implementation.

Implementation of Video Compression Standards in Digital Television

The levels determine the maximum bit rate, and according to the bit rate the speed of transmission of TV programs and resolutions of the system are chosen, and they are, on the other hand, determined by the number of samples per line, number of lines per image and the number of frames per second. Simple profile is designed to simplify the transmitter encoder and receiver decoder, with reductions in binary rate transfer speed , and the inability bidirectional prediction B pictures do not exist supports only I and P prediction.

As such it is suitable only for low-resolution terrestrial television. The main profile is the optimal compromise between compression ratio and price. It supports all three types of prediction I , P , B , which automatically leads to the complexity of the encoder and decoder.

The majority of broadcast applications are scheduled for operation in the main profile. It allows the transfer of basic image quality depending on the spatial resolution spatial or quantization accuracy, with addition of supporting information enhanced layer. This allows simultaneous broadcasting of a program in elementary and higher resolution, so that in case of difficult reception conditions the signal of lower quality can be received lower resolution instead of higher.

High profile also known as professional is designed for later use with hierarchical coding for applications with extremely high definition HDTV—high-definition TV in format sampling or Although, during the development of MPEG-2 standard, studio uses have not been taken into account, it showed that the MPEG-2 standard is suitable for this purpose. Multiview profile MVP is introduced in order to enable efficient coding of two video sequences derived from two cameras which are recording the same scene, and which are set at a slight angle stereovision.

This profile also uses existing tools for encoding, but with a new purpose. There is also reverse compatible decoder which means a higher level still can play lower level profile, while compatibility in the opposite direction is not possible. Present stage of development uses a combination profile and level of main profile at main level.

Video and audio encoders transmit signal in the main stream. Raw uncompressed audio and video parts of the signal, known as presentation units, are located in the encoder for receiving video and audio access units. Video access unit can be I, P and B coded picture. Audio access units are containing encoded information for a few milliseconds of sound window: 24 ms layer II , and 24 or 8 ms in the case of the layer III.

The video and audio access units form the elementary streams in a respective manner. Each elementary stream ES is then divided into packets to form a video or audio packetized elementary stream PES. Service and other data are similarly grouped into their PES.

PES packets are then divided into smaller bit transport packages [ 2 , 10 ]. To gain access to the transfer of MPEG-2 signal, data streams must be multiplexed.

Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications

Multiplexing of audio and video signals is necessary in order to enable their joint transmission, and properly decode and display. Programming flow obtained by multiplexing includes packages resulting from one or more elementary streams belonging to one program. It can contain one stream of the video signal, and more data streams of an audio signal. All packages have certain common components that are grouped into three parts: header, data and control data [ 10 , 11 ]. Packets of the program stream have a variable length, which causes difficulties when the decoder needs to recognize the exact beginning and the end of the package.

To make this possible, the packet header contains information of the length of the package. The part that follows the header contains the access unit as parts of the original elementary stream. At the same time, there is no obligation to equalize the start of access units with a start of information part payload. According to that a new access unit can start at any point in the information part of PES packets, there is also the possibility that a few small access units can be contained in one PES packet.

The most important components of the header are as follows: starting prefix code 3 bits ,. DTS indicates the time required for deleting or decoding access unit.

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Within the headers, some other fields that contain important parameters are included, such as the length of the PES packet, the length of the header and whether the PTS and DTS fields are present in the package. Among this, there are several other optional fields, a total of 25, which can be used to transfer additional information about packetized elementary stream, such as the relative priority and copyright information. In this standard, video and audio signals are characterized by interactivity, high degree of compression, and universal access, and this standard has a high level of flexibility and expandability.

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The algorithms that are implemented in MPEG-4 standard represent scene as a set of audiovisual objects, among which there are some hierarchical relations in space and time. In all previous standards for compression of video, image has been seen as a unified whole.

In this standard, we are meeting with the concept of video object, thereby to distinguish two types of visual objects—natural and synthetic visual objects. At the lowest hierarchical level are primitive media objects, such as, for example, static images fixed background scenes , visual objects a person who speaks no background , and audio facilities voice of the speaker. Each part covers a certain aspect and area of use.

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Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications - CRC Press Book. Download Citation on ResearchGate | Advances in visual data compression and communication: Meeting the requirements of new applications | Visual information is one of To meet the requirements of emerging applications, powerful data.

As in the case of the MPEG-2, coding efficiency is strictly related to the complexity of the source material and the encoder implementation. MPEG-4 Part 2 is defined for applications in the field of multimedia in small bit rates, but it is in further expanded for applications in the field of broadcasting. For presentation of video signal in color, this standard is using a conventional Y Cb Cr color coordinate system with weighing , , and Each component is represented with 4—12 bits per image pixel.

Different temporal resolution is supported, as well as an infinitely variable number of frames per second [ 2 ]. As it was the case in the previous MPEG standards, the macroblock presents basic unit in which data of video signal are transmitted. Macroblock contains coded information about the shape, motion and texture color of the pixels.

Also are supported constant bit rate and variable bit rate. Each video object can be coded in one or more layers, which allows it variable resolution scalability encoding. Also, each video object is discretized in time so that each time samples representing a video object plane VOP [ 2 , 13 , 14 ]. Time samples of video object are grouped into group of video object planes GOV. But with the spread of digital video applications and its use in new applications such as advanced mobile TV or broadcast HDTV signal, requirements for effective representation of the video image are increased to the point where the previous standards for video coding cannot keep pace.

Standard is also commonly referred to as H. A change was added in the MPEG The coded video sequence in the H. The coded image may represent either the entire frame or one field, as was the case with the MPEG-2 codec. Overall, it can be considered that the video frame comprises two fields: the field at the top and the field at the bottom. If the both fields of a given frame were taken at various time points, the frame is called interlaced scan frame; otherwise, it is called a progressive scan frame.

Thanks to the evolution of technology, which has enabled us to have a resolution of video material from 4K and higher reality, the evolution of video coding is inevitable, so it can keep up the step. This means that the video material of the same quality will occupy at half encoding less space with HEVC than the H. Direct predecessor of this standard is H. HEVC seeks to replace its predecessor by using a generic syntax that could be customized to newer emerging applications.

The basic coding algorithm is a hybrid of intraprediction, interprediction and transformational coding. For representation of a color video signal, H. Each sample of the individual components of the color space is represented with a resolution of 8—10 bits per sample, in coding and decoding. A significant difference in approach lies in the fact that the previous H standards—video coding are based on macroblocks, H.

Basically, the quadtree structure is composed of various blocks and units. A block is defined as a matrix of samples of different sizes, while the unit includes a luma block and corresponding chrominance blocks together with the syntax necessary for their coding. With the further division of structure coding units are obtained and also the coding blocks. Decoding of quadtree structure does not represent a significant additional burden because it can easily be switch into a hierarchical structure by using z-scan.

Predictable modes for interframe encoded CU are using non-square PU, which requires the necessary support for decoding in the form of additional logic in the decoder which performs multiple conversions between the raster scan, and the scan-scan. In terms of preserving the speed of bit rates, with the encoder side, there is a simple algorithm to analyze the structure of the tree to determine the optimal share of the blocks [ 7 ]. Profile is defined by a set of coding tools or algorithms which, if used, ensure compatibility of the output coded bit stream with standard applications that belong to this profile, or have similar functional requirements.

Level refers to the limitations of the current stream bits that define memory and resource requirements of the decoder. These restrictions are maximum number of samples and the maximum number of samples per second that can be decoded sample rate , the maximum image size, maximum bit rate how many bits can decoder spend per second of video record , minimum compression ratio, size of the buffer memory and so on.

To determine what information in an audio signal is perceptually irrelevant, most lossy compression algorithms use transforms such as the modified discrete cosine transform MDCT to convert time domain sampled waveforms into a transform domain. Once transformed, typically into the frequency domain , component frequencies can be allocated bits according to how audible they are. Audibility of spectral components calculated using the absolute threshold of hearing and the principles of simultaneous masking —the phenomenon wherein a signal is masked by another signal separated by frequency—and, in some cases, temporal masking —where a signal is masked by another signal separated by time.

Equal-loudness contours may also be used to weight the perceptual importance of components. Models of the human ear-brain combination incorporating such effects are often called psychoacoustic models. Other types of lossy compressors, such as the linear predictive coding LPC used with speech, are source-based coders. These coders use a model of the sound's generator such as the human vocal tract with LPC to whiten the audio signal i. LPC may be thought of as a basic perceptual coding technique: reconstruction of an audio signal using a linear predictor shapes the coder's quantization noise into the spectrum of the target signal, partially masking it.

Lossy formats are often used for the distribution of streaming audio or interactive applications such as the coding of speech for digital transmission in cell phone networks. In such applications, the data must be decompressed as the data flows, rather than after the entire data stream has been transmitted. Not all audio codecs can be used for streaming applications, and for such applications a codec designed to stream data effectively will usually be chosen. Latency results from the methods used to encode and decode the data. Some codecs will analyze a longer segment of the data to optimize efficiency, and then code it in a manner that requires a larger segment of data at one time to decode.

Often codecs create segments called a "frame" to create discrete data segments for encoding and decoding. The inherent latency of the coding algorithm can be critical; for example, when there is a two-way transmission of data, such as with a telephone conversation, significant delays may seriously degrade the perceived quality. In contrast to the speed of compression, which is proportional to the number of operations required by the algorithm, here latency refers to the number of samples that must be analysed before a block of audio is processed. In the minimum case, latency is zero samples e.

Time domain algorithms such as LPC also often have low latencies, hence their popularity in speech coding for telephony. In algorithms such as MP3, however, a large number of samples have to be analyzed to implement a psychoacoustic model in the frequency domain, and latency is on the order of 23 ms 46 ms for two-way communication. Speech encoding is an important category of audio data compression. The perceptual models used to estimate what a human ear can hear are generally somewhat different from those used for music. The range of frequencies needed to convey the sounds of a human voice are normally far narrower than that needed for music, and the sound is normally less complex.

As a result, speech can be encoded at high quality using a relatively low bit rate. If the data to be compressed is analog such as a voltage that varies with time , quantization is employed to digitize it into numbers normally integers. If the integers generated by quantization are 8 bits each, then the entire range of the analog signal is divided into intervals and all the signal values within an interval are quantized to the same number. If bit integers are generated, then the range of the analog signal is divided into 65, intervals. This relation illustrates the compromise between high resolution a large number of analog intervals and high compression small integers generated.

This application of quantization is used by several speech compression methods.

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This is accomplished, in general, by some combination of two approaches:. Cummiskey, Nikil S. Jayant and James L. Flanagan at Bell Labs in Perceptual coding was first used for speech coding compression, with linear predictive coding LPC. Atal and Manfred R. Schroeder at Bell Labs developed a form of LPC called adaptive predictive coding APC , a perceptual coding algorithm that exploited the masking properties of the human ear, followed in the early s with the code-excited linear prediction CELP algorithm which achieved a significant compression ratio for its time.

MDCT was proposed by J. Princen, A. Johnson and A. Bradley in , [33] following earlier work by Princen and Bradley in The world's first commercial broadcast automation audio compression system was developed by Oscar Bonello, an engineering professor at the University of Buenos Aires. Twenty years later, almost all the radio stations in the world were using similar technology manufactured by a number of companies.

While there were some papers from before that time, this collection documented an entire variety of finished, working audio coders, nearly all of them using perceptual i. Video compression is a practical implementation of source coding in information theory. In practice, most video codecs are used alongside audio compression techniques to store the separate but complementary data streams as one combined package using so-called container formats.

Uncompressed video requires a very high data rate. Although lossless video compression codecs perform at a compression factor of 5 to 12, a typical H. The two key video compression techniques used in video coding standards are the discrete cosine transform DCT and motion compensation MC. Most video coding standards, such as the H. Video data may be represented as a series of still image frames. Such data usually contains abundant amounts of spatial and temporal redundancy.

Video compression algorithms attempt to reduce redundancy and store information more compactly. Most video compression formats and codecs exploit both spatial and temporal redundancy e. Similarities can be encoded by only storing differences between e. Inter-frame compression a temporal delta encoding is one of the most powerful compression techniques.

It re uses data from one or more earlier or later frames in a sequence to describe the current frame. Intra-frame coding , on the other hand, uses only data from within the current frame, effectively being still- image compression. A class of specialized formats used in camcorders and video editing use less complex compression schemes that restrict their prediction techniques to intra-frame prediction. Usually video compression additionally employs lossy compression techniques like quantization that reduce aspects of the source data that are more or less irrelevant to the human visual perception by exploiting perceptual features of human vision.

For example, small differences in color are more difficult to perceive than are changes in brightness. Compression algorithms can average a color across these similar areas to reduce space, in a manner similar to those used in JPEG image compression. Highly compressed video may present visible or distracting artifacts. Other methods than the prevalent DCT-based transform formats, such as fractal compression , matching pursuit and the use of a discrete wavelet transform DWT , have been the subject of some research, but are typically not used in practical products except for the use of wavelet coding as still-image coders without motion compensation.

Interest in fractal compression seems to be waning, due to recent theoretical analysis showing a comparative lack of effectiveness of such methods. Inter-frame coding works by comparing each frame in the video with the previous one. Individual frames of a video sequence are compared from one frame to the next, and the video compression codec sends only the differences to the reference frame. If the frame contains areas where nothing has moved, the system can simply issue a short command that copies that part of the previous frame into the next one.

If sections of the frame move in a simple manner, the compressor can emit a slightly longer command that tells the decompressor to shift, rotate, lighten, or darken the copy.

This longer command still remains much shorter than intraframe compression. Usually the encoder will also transmit a residue signal which describes the remaining more subtle differences to the reference imagery. Using entropy coding, these residue signals have a more compact representation than the full signal. In areas of video with more motion, the compression must encode more data to keep up with the larger number of pixels that are changing.

Commonly during explosions, flames, flocks of animals, and in some panning shots, the high-frequency detail leads to quality decreases or to increases in the variable bitrate. Today, nearly all commonly used video compression methods e. They mostly rely on the DCT, applied to rectangular blocks of neighboring pixels, and temporal prediction using motion vectors , as well as nowadays also an in-loop filtering step. In the prediction stage, various deduplication and difference-coding techniques are applied that help decorrelate data and describe new data based on already transmitted data.

Then rectangular blocks of residue pixel data are transformed to the frequency domain to ease targeting irrelevant information in quantization and for some spatial redundancy reduction. The discrete cosine transform DCT that is widely used in this regard was introduced by N. Ahmed , T. Rao in In the main lossy processing stage that data gets quantized in order to reduce information that is irrelevant to human visual perception.

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In the last stage statistical redundancy gets largely eliminated by an entropy coder which often applies some form of arithmetic coding. In an additional in-loop filtering stage various filters can be applied to the reconstructed image signal. By computing these filters also inside the encoding loop they can help compression because they can be applied to reference material before it gets used in the prediction process and they can be guided using the original signal.

The most popular example are deblocking filters that blur out blocking artefacts from quantization discontinuities at transform block boundaries. Basic compression algorithms developed prior to the s provided the basis for modern video compression.

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Traditionally, robots have been used in industry. EE Times. The Main 10 profile allows for improved video quality since it can support video with a higher bit depth than what is supported by the Main profile. HEVC decoders that conform to the Main 12 profile must be capable of decoding bitstreams made with the following profiles: Monochrome, Monochrome 12, Main, Main 10, and Main Of half, no one is you control to submit too a commission scope when you have.

Entropy coding started in the s with the introduction of Shannon—Fano coding , [42] the basis for Huffman coding which was developed in Robinson and C. Cherry proposed a run-length encoding bandwidth compression scheme for the transmission of analog television signals. The most popular video coding standards used for codecs have been the MPEG standards. The most widely used video coding format is H. Genetics compression algorithms are the latest generation of lossless algorithms that compress data typically sequences of nucleotides using both conventional compression algorithms and genetic algorithms adapted to the specific datatype.

In , a team of scientists from Johns Hopkins University published a genetic compression algorithm that does not use a reference genome for compression. It is estimated that the total amount of data that is stored on the world's storage devices could be further compressed with existing compression algorithms by a remaining average factor of 4.

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From Wikipedia, the free encyclopedia. For the term in computer programming, see Source code. Process of encoding information using fewer bits than the original representation. Main article: Lossless compression. Main article: Lossy compression. See also: Machine learning. Main article: Data differencing. See also: Audio codec and Audio coding format. See also: Video coding format and Video codec. Main article: Inter frame. Further information: Motion compensation. Further information: Discrete cosine transform. Signal coding and processing 2 ed.

Cambridge University Press. Retrieved The broad objective of source coding is to exploit or remove 'inefficient' redundancy in the PCM source and thereby achieve a reduction in the overall source rate R. November International Journal of Computer Science Issues. Retrieved 6 March May Journal of Theoretical and Applied Information Technology. A Concise Introduction to Data Compression. Berlin: Springer. Mittal; J. April Electrical Review.

A New Kind of Science. Wolfram Media, Inc. Internet FAQ Archives. Sullivan ; J. Ohm; W. Han; T. Wiegand December Digital Signal Processing. Natarajan; Kamisetty Ramamohan Rao January C 1 : 90— University of Marne la Vallee. Archived from the original PDF on 28 May Florida Institute of Technology. Retrieved 5 March Computational Economics: — Ben-Gal Scully; Carla E.