barcode in vb.net The JPEG Standard in Software

Maker Code 128B in Software The JPEG Standard

The JPEG Standard
Read Code 128 In None
Using Barcode Control SDK for Software Control to generate, create, read, scan barcode image in Software applications.
Code 128 Generation In None
Using Barcode printer for Software Control to generate, create Code 128 Code Set B image in Software applications.
Before compression schemes for video were being standardized, the Joint Picture Experts Group (JPEG) was formed to standardize the compression of static images. Video compression standards, such as MPEG-1, MPEG-2, and MPEG-4, can trace their origins to this standards effort. The JPEG standard is enjoying widespread commercial use today in a bewildering variety of applications. Because JPEG is the product of a committee, it isn t surprising that it includes more than one fixed encoding/decoding scheme. It can be thought of as a family, or as related compression techniques, from which designers can choose, based upon suitability for their particular application. The four primary JPEG family members are [2]:
Code128 Reader In None
Using Barcode scanner for Software Control to read, scan read, scan image in Software applications.
Drawing Code 128 In C#
Using Barcode generation for VS .NET Control to generate, create Code 128C image in .NET framework applications.
Sequential DCT-based Progressive DCT-based
Draw Code 128 Code Set C In .NET
Using Barcode generation for ASP.NET Control to generate, create Code-128 image in ASP.NET applications.
Code-128 Encoder In Visual Studio .NET
Using Barcode printer for Visual Studio .NET Control to generate, create Code128 image in .NET framework applications.
Video and Audio Compression
USS Code 128 Creation In Visual Basic .NET
Using Barcode generator for VS .NET Control to generate, create Code-128 image in Visual Studio .NET applications.
Barcode Creation In None
Using Barcode drawer for Software Control to generate, create barcode image in Software applications.
Figure E.4 Overall block diagram of a DPCM system: (a) encoder, (b) decoder. (From [2]. Used with permission.)
EAN 13 Printer In None
Using Barcode maker for Software Control to generate, create GS1 - 13 image in Software applications.
Paint DataMatrix In None
Using Barcode encoder for Software Control to generate, create Data Matrix image in Software applications.
Sequential lossless Hierarchical
Bar Code Generator In None
Using Barcode drawer for Software Control to generate, create bar code image in Software applications.
Making USS-128 In None
Using Barcode generation for Software Control to generate, create GS1 128 image in Software applications.
As JPEG has been adapted to other environments, additional JPEG schemes have come into practice. JPEG is designed for still images and offers reduction ratios of 10:1 to 50:1. The algorithm is symmetrical, meaning that the time required for encoding and decoding is essentially the same. There is no need for motion compensation and there are no provisions for audio in the basic standard. The JPEG specification, like MPEG-1 and MPEG-2, is often described as a tool kit of compression techniques. Before looking at specifics, it will be useful to examine some of the basics. Remember that many of these underpin current streaming media compression techniques.
GTIN - 12 Encoder In None
Using Barcode generation for Software Control to generate, create UCC - 12 image in Software applications.
Scanning EAN-13 In Visual Basic .NET
Using Barcode reader for .NET Control to read, scan read, scan image in .NET applications.
Compression Techniques
Making DataMatrix In None
Using Barcode drawer for Online Control to generate, create Data Matrix image in Online applications.
Making UPC Symbol In None
Using Barcode encoder for Font Control to generate, create UPCA image in Font applications.
Appendix E
Generating UPC A In Visual C#.NET
Using Barcode drawer for VS .NET Control to generate, create GTIN - 12 image in .NET framework applications.
Encode Code 128 Code Set A In Objective-C
Using Barcode maker for iPad Control to generate, create Code 128 Code Set C image in iPad applications.
As discussed briefly in previous sections, a compression system reduces the volume of data by exploiting spatial and temporal redundancies and by eliminating the data that cannot be displayed suitably by the associated display or imaging device. The main objective of compression is to retain as little data as possible, but just sufficient to reproduce the original images without causing unacceptable distortion of images [1]. To paraphrase Albert Einstein, the aim is to make things as simple as possible, but no simpler. A compression system consists of the following components:
Code 39 Creator In VB.NET
Using Barcode drawer for Visual Studio .NET Control to generate, create Code 39 Full ASCII image in VS .NET applications.
Generate 1D In C#
Using Barcode drawer for Visual Studio .NET Control to generate, create Linear Barcode image in .NET framework applications.
Digitization, sampling, and segmentation Steps that convert analog signals on a specified grid of picture elements into digital representations and then divide the video input first into frames, then into blocks. Redundancy reduction The decorrelation of data into fewer useful data bits using certain invertible transformation techniques. Entropy reduction The representation of digital data using fewer bits by dropping less significant information. This component causes distortion; it is the main contributor to lossy compression. Entropy coding The assignment of code words (bit strings) of shorter length to more likely image symbols. This minimizes the average number of bits needed to code an image.
Key terms important to understanding video compression include the following:
Motion compensation The coding of video segments with consideration to their displacements in successive frames (in other words, coding segments of the picture according to how they move over a series of frames). Spatial correlation The correlation of elements within a still image or a video frame for the purpose of bit rate reduction (in other words, looking for segments of the picture that are the same or similar, so that you don t need to fully describe each and every similar segment, you can just instruct the decoder to duplicate a single fully described segment). Spectral correlation The correlation of different color components of image elements for the purpose of bit rate reduction (you guessed it looking for segments of the picture than have the same or similar
Video and Audio Compression
color, so that you don t need to fully describe the color for each instance). Temporal correlation The correlation between successive frames of a video file for the purpose of bit rate reduction (or to paraphrase, looking for parts of successive video frames that are the same or similar, so that you don t need to fully describe those features in every frame you can just instruct the decoder to obtain the data from a single frame to decode successive frames). Quantization compression The dropping of the less significant bits of image values to achieve higher compression (this is tantamount to picking colors that are near enough to the actual colors, but which can be described with fewer bits). Intraframe coding The encoding of a video frame by exploiting spatial redundancy within the frame. Once you have found spatial correlations, you code so that you describe duplicated picture regions only once. Interframe coding The encoding of a frame by predicting its elements from elements of the previous frame. The idea is to describe a visual element just once and then pass information about how it moves (translates) in two dimensions.
The removal of spatial and temporal redundancies that exist in natural video imagery is essentially a lossless process. Given the correct techniques, an exact replica of the image can be reproduced at the viewing end of the system. Such lossless techniques are important for medical imaging applications and other demanding uses. These methods, however, may realize only low compression efficiency (on the order of approximately 2:1). For video, a much higher compression ratio is required. Exploiting the inherent limitations of the human visual system (HVS) can result in compression ratios of 50:1 or higher [6]. These limitations include the following:
Limited luminance response and very limited color response. There are more colors than we can perceive and there is a greater range of light than we can see. If we could see a greater range of luminance, we would be better able to discriminate objects in the dark and to differentiate objects in very bright glare, for example. Reduced sensitivity to noise in high frequencies, such as at the edges of objects. Reduced sensitivity to noise in brighter areas of the image
Copyright © OnBarcode.com . All rights reserved.