Figure 12.1 shows a compression system consisting of two distinct structural blocks: an encoder and a decoder. An input image f(x, y) is fed into the decoder, which creates a set of symbols from the input data. After transmission over the channel, the encoded representation is fed to the decoder, where a reconstructed output image clip_image018[2] (x, y) is generated. In general, clip_image018[3] (x, y) may or may not be an exact replica of f(x, y). If it is, the system is error free for information preserving; if not, some level of distortion is present in the reconstructed image.
Both the encoder and decoder shown in Figure (12.1) consist of two relatively independent functions or sub blocks. The encoder is made up of a source encoder, which removes input redundancies, and a channel encoder, which increases the noise immunity of the source encoder s output. The decoder includes a channel decoder followed by a source decoder. If the channel between the encoder and decoder is noise free (not prone to error), the channel encoder and decoder are omitted, and the general encoder and decoder become the source encoder and decoder, respectively.
If you have book of BCA manipal university 6th sem (Image Processing)see in Page 176 for Diagram:
The Source Encoder and Decoder
The source is responsible for reducing or eliminating any coding, interpixel or psychovisual redundancies in the input image. The specific application and associated fidelity requirements dictate the best encoding approach to use in any given situation. Normally, the approach can be modeled by a series of three independent operations. As shown in Figure 12.2 (a), each operation is designed to reduce one of the three redundancies. Figure 12.2(b) depicts the corresponding source decoder.
If you have book of BCA manipal university 6th sem (Image Processing)see in Page 176 for Diagram:
In the first stage of the source encoding process, the mapper transforms the input data into a format to reduce interpixel redundancies in the input image. This operation generally is reversible and may or may not reduce directly the amount of data required to represent the image. Run-length coding is an example of a mapping that directly results in data compression in this initial state of the overall source encoding process. The representation of an image by a set of transforms coefficients is an example of the opposite case. Here the mapper transforms the image into an array of coefficients, making its interpixel redundancies more accessible for compression in later stages of the encoding process.
In the second stage, the quantizer block, reduces the accuracy of the mapper s output in accordance with some pre-established fidelity criterion. This stage reduces the psycho visual redundancies of the input image. As this operation is irreversible, thus it must be omitted when error-free compression is desired.
In the third and final stage, the symbol coder creates a fixed or variable-length code to represent the quantizer output and maps the output in accordance with the code. The term source coder distinguishes this coding operation from the overall source encoding process. In most cases, a variable-length code is used to represent the mapped and quantized data set. It assigns the shortest code words to the most frequently occurring output values and thus reduces coding redundancy. The operation, of course, is reversible. Upon completion of the symbol coding step, the input image has been processed to remove each of the three redundancies.
Figure 12.2(a) shows the source encoding process as three successive operations, but all the operations are not necessarily included in every compression system.
The source decoder shown in Figure12.2(b) contains only two components; a symbol decoder and an inverse mapper. These blocks perform the inverse operations of the source encoder s symbol encoder and mapper blocks. Because quantization results in irreversible information loss, an inverse quantizer block is not included in the general source decoder model.
Both the encoder and decoder shown in Figure (12.1) consist of two relatively independent functions or sub blocks. The encoder is made up of a source encoder, which removes input redundancies, and a channel encoder, which increases the noise immunity of the source encoder s output. The decoder includes a channel decoder followed by a source decoder. If the channel between the encoder and decoder is noise free (not prone to error), the channel encoder and decoder are omitted, and the general encoder and decoder become the source encoder and decoder, respectively.
If you have book of BCA manipal university 6th sem (Image Processing)see in Page 176 for Diagram:
The Source Encoder and Decoder
The source is responsible for reducing or eliminating any coding, interpixel or psychovisual redundancies in the input image. The specific application and associated fidelity requirements dictate the best encoding approach to use in any given situation. Normally, the approach can be modeled by a series of three independent operations. As shown in Figure 12.2 (a), each operation is designed to reduce one of the three redundancies. Figure 12.2(b) depicts the corresponding source decoder.
If you have book of BCA manipal university 6th sem (Image Processing)see in Page 176 for Diagram:
In the first stage of the source encoding process, the mapper transforms the input data into a format to reduce interpixel redundancies in the input image. This operation generally is reversible and may or may not reduce directly the amount of data required to represent the image. Run-length coding is an example of a mapping that directly results in data compression in this initial state of the overall source encoding process. The representation of an image by a set of transforms coefficients is an example of the opposite case. Here the mapper transforms the image into an array of coefficients, making its interpixel redundancies more accessible for compression in later stages of the encoding process.
In the second stage, the quantizer block, reduces the accuracy of the mapper s output in accordance with some pre-established fidelity criterion. This stage reduces the psycho visual redundancies of the input image. As this operation is irreversible, thus it must be omitted when error-free compression is desired.
In the third and final stage, the symbol coder creates a fixed or variable-length code to represent the quantizer output and maps the output in accordance with the code. The term source coder distinguishes this coding operation from the overall source encoding process. In most cases, a variable-length code is used to represent the mapped and quantized data set. It assigns the shortest code words to the most frequently occurring output values and thus reduces coding redundancy. The operation, of course, is reversible. Upon completion of the symbol coding step, the input image has been processed to remove each of the three redundancies.
Figure 12.2(a) shows the source encoding process as three successive operations, but all the operations are not necessarily included in every compression system.
The source decoder shown in Figure12.2(b) contains only two components; a symbol decoder and an inverse mapper. These blocks perform the inverse operations of the source encoder s symbol encoder and mapper blocks. Because quantization results in irreversible information loss, an inverse quantizer block is not included in the general source decoder model.
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