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Image Compression Final presentaion
Transcript of Image Compression Final presentaion
Basic types of compression:
Image Compression for Medical Image
Why we need image compression ?
A Brain MRI Representation
Lossless compression :
Lossy coding techniques :
(Digital Imaging and Communications in Medicine)
* Save more storage space
* Save more time to transfer image
* Reduce cost
* Lossless compression
* Lossy compression
* Data is compressed without any loss of data.
Lossy compression :
* It is assumed that some loss of information is acceptable.
* High quality images like JPEG 2000
* Low quality images like JPEG
In practice mostly …
Differentiate between lossy and lossless
Entropy coding (LZW Coding) :
New approach and preliminary results...
* Lossless JPEG for cardiac
- Multi-frame 512x512x8, 1024x1024x10
* Lossless JPEG for CT/MR
- 256x256 to 1024x1024, 12-16 bits
* RLE/lossless/lossy JPEG for Ultrasound
- 640x480 single and multi-frame 8 bits gray/RGB, text
* Transform coding
* Vector quantization
* Fractal coding
* Block truncation coding
* Sub band coding
* Huffman tree:
Save in HDD in array of JPEG
or one JPEG2000(DICOM)
Run length encoding :
Huffman encoding :
Advantages and disadvantages :
Or Upload to Cloud system in array of JPEG
* Supported by most bitmap file formats, such as TIFF, BMP, and PCX.
RLE algorithms cannot achieve the high compression ratios of the more advanced compression methods
* Simple to implement.
* Widely used Unix file compression utility compress
* Used in the GIF image format.
* Run length encoding
* Huffman encoding
* Entropy coding (LZW Coding)
* Support all extension of images and videos
* Encoding table is built by analyzing the
entire document, before the compression step
* Huffman code is a prefix code
Sub band coding
Block truncation coding
* Fast implementation of the DCT(Discrete Cosine Transform)
* Consume little time in bandwith
* resulting in a lower quality copy of the original input.
* This technique is to develop a dictionary of fixed-size vectors
* A given image is then partitioned into non-overlapping blocks (vectors) called image vectors
* Each image is represented by a sequence of indices that can be further entropy coded
* Based on fractals
* Fractal algorithms convert these parts into mathematical data called "fractal codes" which are used to recreate the encoded image.
* This technique for grey scale images
* Using sub-blocks of 4x4 pixels gives a compression ratio of 4:1 assuming 8-bit integer values are used during transmission or storage
* In signal processing
* Transform coding that breaks a signal into a number of different frequency bands and encodes each one independently
* Compression for audio and video signals.
* Medical Image have a high resolution too hard to loss
* Medical Image have important details
* Save time to transfer
* In this application we use a hybrid technique and save more than 70% of total space of image