Shannon and Weaver's Model of Transmission

Shannon and Weaver's model is one which is, in John Fiske's words, 'widely accepted as one of the main seeds out of which Communication Studies has grown' (Fiske 1982: 6). Claude Shannon and Warren Weaver were not social scientists but engineers working for Bell Telephone Labs in the United States. Their goal was to ensure the maximum efficiency of telephone cables and radio waves. They developed a model of communication which was intended to assist in developing a mathematical theory of communication. Shannon and Weaver's work proved valuable for communication engineers in dealing with such issues as the capacity of various communication channels in 'bits per second'. It contributed to computer science. It led to very useful work on redundancy in language. And in making 'information' 'measurable' it gave birth to the mathematical study of 'information theory'. However, these directions are not our concern here. The problem is that some commentators have claimed that Shannon and Weaver's model has a much wider application to human communication than a purely technical one.

C & W's original model consisted of five elements:


1. An information source, which produces a message.
2. A transmitter, which encodes the message into signals
3. A channel, to which signals are adapted for transmission
4. A receiver, which 'decodes' (reconstructs) the message from the signal.
5. A destination, where the message arrives.
A sixth element, noise is a dysfunctional factor: any interference with the message travelling along the channel (such as 'static' on the telephone or radio) which may lead to the signal received being different from that sent.
Example 1: For the telephone the channel is a wire, the signal is an electrical current in it, and the transmitter and receiver are the telephone handsets. Noise would include crackling from the wire.
Example 2: In conversation, my mouth is the transmitter, the signal is the sound waves, and your ear is the receiver. Noise would include any distraction you might experience as I speak.
Shannon and Weaver's transmission model is the best-known example of the 'informational' approach to communication. Although no serious communication theorist would still accept it, it has also been the most influential model of communication which has yet been developed, and it reflects a commonsense (if misleading) understanding of what communication is. Lasswell's verbal version of this model: 'Who says what in which channel to whom with what effect ?' was reflected in subsequent research in human communication which was closely allied to behaviouristic approaches.
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Lossless and Lossy Image Compression

Lossless and lossy compression are terms that describe whether or not, in the compressionof a file, all original data can be recovered when the file is uncompressed. With lossless compression, every single bit of data that was originally in the file remains after the file is uncompressed. All of the information is completely restored. This is generally the technique of choice for text or spreadsheet files, where losing words or financial data could pose a problem. The Graphics Interchange File (GIF) is an image format used on the Web that provides lossless compression.

On the other hand, lossy compression reduces a file by permanently eliminating certain information, especially redundant information. When the file is uncompressed, only a part of the original information is still there (although the user may not notice it). Lossy compression is generally used for video and sound, where a certain amount of information loss will not be detected by most users. The JPEG image file, commonly used for photographs and other complex still images on the Web, is an image that has lossy compression. Using JPEG compression, the creator can decide how much loss to introduce and make a trade-off between file size and image quality.
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In information technology, "lossy" compression is a data encoding method that compresses data by discarding (losing) some of it. The procedure aims to minimize the amount of data that needs to be held, handled, and/or transmitted by a computer. The different versions of the photo of the dog at the right demonstrate how much data can be dispensed with, and how the images become progressively coarser as the data that made up the original one is discarded (lost). Typically, a substantial amount of data can be discarded before the result is sufficiently degraded to be noticed by the user.

Lossy compression is most commonly used to compress multimedia data (audio, video, and still images), especially in applications such as streaming mediaand internet telephony. By contrast, lossless compression is required for text and data files, such as bank records and text articles. In many cases it is advantageous to make a master lossless file that can then be used to produce compressed files for different purposes; for example, a multi-megabyte file can be used at full size to produce a full-page advertisement in a glossy magazine, and a 10 kilobyte lossy copy can be made for a small image on a web page.

Lossy and lossless compression

It is possible to compress many types of digital data in a way that reduces the size of a computer file needed to store it, or the bandwidth needed to stream it, with no loss of the full information contained in the original file. A picture, for example, is converted to a digital file by considering it to be an array of dots and specifying the color and brightness of each dot. If the picture contains an area of the same color, it can be compressed without loss by saying "200 red dots" instead of "red dot, red dot, ...(197 more times)..., red dot."

The original contains a certain amount of information; there is a lower limit to the size of file that can carry all the information. As an intuitive example, most people know that a compressed ZIP file is smaller than the original file, but repeatedly compressing the file will not reduce the size to nothing and will in fact usually increase the size.

In many cases, files or data streams contain more information than is needed for a particular purpose. For example, a picture may have more detail than the eye can distinguish when reproduced at the largest size intended; likewise, an audio file does not need a lot of fine detail during a very loud passage. Developing lossy compression techniques as closely matched to human perception as possible is a complex task. Sometimes the ideal is a file that provides exactly the same perception as the original, with as much digital information as possible removed; other times, perceptible loss of quality is considered a valid trade-off for the reduced data

Index and Alpha Transparency

Index Transparency

Index transparency is the simpler. A GIF file has two parts: a color table and the image pixel data. The color table is a list of the colors used in that image (an 8-bit GIF can have up to 2^8 = 256 colors in the color table, but a 4-bit GIF can have only 2^4 = 16 colors), and each color is assigned a number. The image pixel data are for the image itself, and each pixel is assigned a number that points to its color in the color table. For example, if color #10 in the color table is red (#FF0000), then any pixel in the image with the number 10 will be displayed as red. The colors in the color table will vary from GIF file to GIF file based on the image itself; color #10 will not always be red. The color table is the set of up to 256 colors necessary to render that image.

When we add index transparency, every color in the color table is given a transparency designation in addition to its color data  (i.e., RGB values):

• zero (o = False in Boolean algebra) means do not display this color, or
• one (1 = True in Boolean Algebra) means display this color.

There are no intermediate opacities; the color is either displayed or it is not. The end result is that a pixel with an index transparency color will not be displayed and whatever is in the background behind that pixel will show through. For example, if color #10 is red (#FF0000) and is designated as transparent (index transparency = 0), then any pixel that is color #10 will not be displayed and the background will show through.

There can be multiple transparent colors in index transparency, because every color in the color table has a designation of either opaque (1) or transparent (0). Most graphics programs assume that the canvas color (often white, but it could be any color) is the default transparent color, but you can specify any color (or any number of colors) as transparent or not.

This type of transparency is very common in GIF and PNG8 files and is easy to identify, because there is no fading, there are no partially transparent pixels, and the edges are often described as “hard” or “pixelated.”

Alpha Transparency

Alpha transparency is more complex. Every color in a graphic has an alpha channel value to indicate how transparent it is (i.e., the degree of opacity.) When you see references to the RGBA color space, the color is defined by the (R)ed, (G)reen, and (B)lue channels, and the (A)lpha value defines the opacity for that color where:

• zero (0) = completely transparent (there is 0% rendering of that pixel’s color – all of the background “shows through”), and
• one (1) = completely opaque (there is 100% rendering of that pixel’s color – none of the background “shows through.”)

Alpha transparency is a way to support gradations of opacity on a continuum. Values between zero (0) and one (1) allow some of the background to “show through” and “mix” with that color. Values closer to zero (0) show more background, and values closer to one (1) show less background. A range of opacity makes it possible to create graphics that fade away or have smoother edges.

PNG32, PNG24, and Alpha Transparency

In Adobe Fireworks, alpha transparency is most often used with the PNG32 format. In a PNG32 file, there are four bytes (8 bits per byte) for each color: (R)ed, (G)reen, (B)lue, and (A)lpha, which adds up to 32 bits per color (24 bits for color and 8 bits for opacity via the alpha channel.) This means that alpha transparency in a PNG32 file is one (1) byte (8 bits) and can have a value from 0 (0%) to 255 (100%). Each step in the opacity continuum for 8-bit alpha transparency equals a 0.392% change in opacity; this is small enough that our eyes see a relatively smooth change along that opacity continuum.

Note that Adobe Photoshop uses the name PNG24 for the same 32-bit file type, because it only counts  the three RGB color channels, but a Photoshop PNG24does support alpha transparency (and really should be called a PNG32, because each color has four bytes, or 32 bits, of color and opacity data.) The PNG24 format in Fireworks includes only the three RGB color channels and does not support anytransparency.

PNG8, GIF, and Alpha Transparency

You may also have seen references to alpha transparency support in PNG8 files exported from Adobe Fireworks. For many years this has been a way to display alpha transparent images more cleanly in Internet Explorer 6, because Internet Explorer 6 does not support PNG32 with alpha transparency, but it treats a PNG8 file with alpha transparency like a GIF with index transparency. The result is not as smooth and aesthetically pleasing as true alpha transparency, but it also avoids the “grey box of doom” that Internet Explorer 6 would normally display for the PNG32 with alpha transparency file. Luckily, Internet Explorer is no longer a dominant browser, but there are enough people still using it that this PNG8 technique is still useful.

So, how does PNG8 support alpha transparency? It adds a four (4) bit alpha channel to the color table for the PNG8 file. This increases the file size slightly, and there are only 16 levels in the opacity continuum (2^4 = 16 levels, where 2^8 = 256 levels for PNG32 alpha transparency), so a gradual fade is more likely to look pixelated or exhibit banding.

If you look in the Optimize panel in Adobe Fireworks, you will notice that the GIF file format has three choices for transparency: None, Index, and Alpha. However, GIF files do not support alpha transparency, so the Index and Alpha options work exactly the same way. (I suppose that alpha transparency should not even be offered as an option for the GIF format, since GIF only supports index transparency.)

Creating Images with Alpha Transparency

The most obvious examples of alpha transparency are glows and shadows: they seem to fade as they get farther from the object, and when placed over a background pattern or image you can see that more and more background “shows through” as the glow or shadow gets lighter.

You can also create an opacity continuum with alpha transparency by:

• Creating a gradient fill and setting the opacity at one end to 100% and to 0% at the other, and you can even create bands of transparency by inserting multiple opacity points along the gradient:

• Applying a grayscale vector mask to an image:

Using a grayscale mask works by calculating the opacity of an image pixel based on the amount of white in the mask pixel:

• If the mask pixel has 0% white (i.e., it is black, #000000), then 0% of the image pixel is shown (i.e., the image becomes completely transparent),
• If the mask pixel has 100% white (i.e., it is white, #FFFFFF), then 100% of the image pixel is shown (i.e., the image is completely opaque),
• The shades of grey between black and white produce graduated opacity, and the image appears to fade from transparent to opaque along a gradual continuum.

Filter: Convert to Alpha

Finally, Adobe Fireworks provides the ability to take any image or object and calculate the alpha transparency based on the luminosity of each color by adding a filter (Filters > Other > Convert to Alpha.) The lighter the original color, the more transparent it becomes. The darker the original color, the less transparent it becomes. The end result is a grayscale image where lighter grays are more transparent than darker grays. This is useful if you want to create a mask to help isolate or extract a portion of an image or superimpose one image over another and have the two images appear to blend.

Photographs, JPEGs, and Transparency

Most images with transparency are graphics like logos and icons. There is a reason we rarely see transparency with photographs: the consolidation of colors in the color table for GIF and PNG8 files reduces the number of colors from many to a few.

When you have a photograph, there is a wide range of colors – usually far more than the 256 limit imposed by an 8-bit image format – so when you constrain the color palette of an image to 256 or fewer colors, the graphics program must calculate which colors are near one another and then assign them all to the same color value. For example, anything that is “near white” will be assigned to “white”, and when white is a transparent color, anything “near white” in the original image will also become transparent. This is simply the way that any 8-bit image format works – 8 bits only allow for 256 colors, so all “near colors” are combined into a single color value. All of the “near reds” become red, all “near blues” become blue, etc. This makes it possible to represent the image relatively accurately with far fewer colors.

This is also why 8-bit formats (GIF, PNG8) are not good for photographs: there are simply too many colors in most photographs to be accurately rendered with 256 or fewer colors. The JPEG format allows for many more colors, but it does not support transparency, and it is a lossy format (i.e., it uses compression algorithms that compress the image data, therefore some original image data are lost.)

Source: http://www.idux.com/2011/02/27/what-are-index-and-alpha-transparency/