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COMPRESSION MODELSCOMPRESSION MODELSCOMPRESSION MODELS
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- 2. WHAT IS COMPRESSION? WHAT IS COMPRESSION ? • Image compression is the process of reducing amount of data Image compression is the process of reducing amount of data required to represent or store an image. required to represent or store an image. • Process of encoding data so that it take less storage space or Process of encoding data so that it take less storage space or less transmission time. less transmission time.
- 3. Why Do WeNeed Image Compression? Why Do We Need Image Compression? • Consider a black and white image that has a resolution of Consider a black and white image that has a resolution of 1000*1000 1000*1000 • each pixel uses 8 bits to represent the intensity. each pixel uses 8 bits to represent the intensity. • So total no of bits required = 1000*1000*8 = 80,00,000 bits per So total no of bits required = 1000*1000*8 = 80,00,000 bits per image. image. • And consider if it is a video with 30 frames per second of the And consider if it is a video with 30 frames per second of the above-mentioned type images above-mentioned type images • total bits for a video of 3 secs is: 3*(30*(8, 000, 000)) total bits for a video of 3 secs is: 3*(30*(8, 000, 000)) • =720, 000, 000 bits =720, 000, 000 bits
- 4. • Storage Efficiency:Compressed images require less storage Storage Efficiency: Compressed images require less storage space. space. • Transmission Speed: Smaller image files can be transmitted Transmission Speed: Smaller image files can be transmitted faster over networks. faster over networks. • Cost Reduction: Less storage and bandwidth usage lead to cost Cost Reduction: Less storage and bandwidth usage lead to cost savings. savings. • Improved Performance: Enhances performance of applications Improved Performance: Enhances performance of applications by reducing load times. by reducing load times.
- 5. Data vs. Information Datavs. Information • Data: Raw pixel values in an image. • Information: Meaningful content derived from data. • Compression focuses on reducing data redundancy while preserving essential information.
- 6. Redundancy and ItsTypes Redundancy and Its Types •Redundancy means Redundancy means repetitive data or repetitive data or unwanwanted data unwanwanted data CLASSIFICATION CLASSIFICATION Interpixel Redundancy Interpixel Redundancy Psychovisual Redundancy Psychovisual Redundancy Coding Redundancy Coding Redundancy
- 7. Coding Redundancy Coding Redundancy Coding redundancy is caused due to poor selection of coding Coding redundancy is caused due to poor selection of coding technique technique Coding techniques assigns a unique code for all symbols of message Coding techniques assigns a unique code for all symbols of message • Wrong choice of coding technique creates unnecessary additional Wrong choice of coding technique creates unnecessary additional bits. These extra bits are called redundancy bits. These extra bits are called redundancy • CODING REDUNDANCY = AVERAGE BITS USED TO CODE - ENTROPHY CODING REDUNDANCY = AVERAGE BITS USED TO CODE - ENTROPHY
- 8. Interpixel Redundancy Interpixel Redundancy •This type of redundancy is related with the inter-pixel This type of redundancy is related with the inter-pixel correlations within an image. correlations within an image. • The value of any given pixel can be predicted from the value of The value of any given pixel can be predicted from the value of its neighbours or adjacent pixels that are highly corelated. its neighbours or adjacent pixels that are highly corelated. • Inter-pixel dependency is solved by algorithms like: Inter-pixel dependency is solved by algorithms like: • Predictive Coding, Bit Plane Algorithm, Run Length Predictive Coding, Bit Plane Algorithm, Run Length
- 9. Psychovisual Redundancy Psychovisual Redundancy •The eye and the brain do not respond to all visual information with The eye and the brain do not respond to all visual information with same sensitivity. same sensitivity. • Some information is neglected during the processing by the Some information is neglected during the processing by the brain.because human perception does not involve quantative analysis brain.because human perception does not involve quantative analysis of every pixel in the image. of every pixel in the image. • Elimination of this information does not affect the interpretation of Elimination of this information does not affect the interpretation of the image by the brain. the image by the brain. • Psycho visual redundancy is distinctly vision related, and its Psycho visual redundancy is distinctly vision related, and its elimination does result in loss of information. elimination does result in loss of information. • Quantization is an example. When 256 levels are reduced by grouping Quantization is an example. When 256 levels are reduced by grouping to 16 levels, objects are still recognizable. to 16 levels, objects are still recognizable.
- 10. Image Compression Model ImageCompression Model • Encoder: Compresses the Encoder: Compresses the image by reducing image by reducing redundancies redundancies • Decoder: Reconstructs the Decoder: Reconstructs the image from compressed image from compressed data data
- 11. BLOCK DIAGRAM OFCOMPRESSION MODEL BLOCK DIAGRAM OF COMPRESSION MODEL
- 12. Stages of Encoder Stagesof Encoder •MAPPER MAPPER Reduces Interpixel Redundancy Reduces Interpixel Redundancy Reversible operation Reversible operation QUANTIZER QUANTIZER Reduces Psychovisual Redundancy Reduces Psychovisual Redundancy Not a reversible operation Not a reversible operation SYMBOL ENCODER SYMBOL ENCODER To create a fixed or variable length code To create a fixed or variable length code Reversible operation Reversible operation
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- 24. JPEG Data compression JPEGData compression • Joint Photographic Experts Group : lossy compression Joint Photographic Experts Group : lossy compression
- 25. Algorithm of JPEGData Compression : Algorithm of JPEG Data Compression : 1. 1.Splitting Splitting – We split our image into the blocks of 8*8 blocks. It forms – We split our image into the blocks of 8*8 blocks. It forms 64 blocks in which each block is referred to as 1 pixel. 64 blocks in which each block is referred to as 1 pixel. 2. 2.Color Space Transform Color Space Transform – In this phase, we convert R, G, B to Y, Cb, – In this phase, we convert R, G, B to Y, Cb, Cr model. Here Y is for brightness, Cb is color blueness and Cr Cr model. Here Y is for brightness, Cb is color blueness and Cr stands for Color redness. We transform it into chromium colors as stands for Color redness. We transform it into chromium colors as these are less sensitive to human eyes thus can be removed. these are less sensitive to human eyes thus can be removed. 3. 3.Apply DCT Apply DCT – We apply Direct cosine transform on each block. The – We apply Direct cosine transform on each block. The discrete cosine transform (DCT) represents an image as a sum of discrete cosine transform (DCT) represents an image as a sum of sinusoids of varying magnitudes and frequencies. sinusoids of varying magnitudes and frequencies.
- 26. 4.Quantization 4.Quantization – reducethe no of bit per sample – reduce the no of bit per sample 5. Serialization – 5. Serialization – In serialization, we perform the zig-zag scanning In serialization, we perform the zig-zag scanning pattern to exploit redundancy. pattern to exploit redundancy. 6. Vectoring 6. Vectoring – We apply DPCM (differential pulse code modeling) on DC – We apply DPCM (differential pulse code modeling) on DC elements. DC elements are used to define the strength of colors. elements. DC elements are used to define the strength of colors. 7.Encoding 7.Encoding – – •In the last stage, we apply to encode either run-length encoding or In the last stage, we apply to encode either run-length encoding or Huffman encoding. The main aim is to convert the image into text and Huffman encoding. The main aim is to convert the image into text and by applying any encoding we convert it into binary form (0, 1) to by applying any encoding we convert it into binary form (0, 1) to compress the data. compress the data.