Lossless compressionLossless compression scheme is a compression method of images that allows the original data to be perfectly reconstructed from the compressed data. Although lossless compression scheme, may yield much greater quality images than lossy compression schemes, its extent of compression ratios generally are much smaller so file sizes cannot be reduced nearly as much as in lossy compression.
Lossless compression scheme is very important in the world of medical imaging because the accuracy of images being exchange must remain appropriate for proper diagnoses to be performed. In the digital age of medical imaging, image archiving hospital systems like PACS requires lossless compression scheme with high efficiency to perform at optimal levels. Research into more efficient compression while maintain medical image application is an emerging and growing field. |
Different types of lossless compression
There are three main types of lossless compression schemes:
1. Prediction-based compression
2. Entropy-based compression
3. Dictionary-based compression
1. Prediction-based compression
2. Entropy-based compression
3. Dictionary-based compression
Prediction-based compression use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. One example is the gradient adjusted predictor. From one pixel, the surrounding pixel are recorded as x derivation. This in turn generates many values with many 0s. This can be greatly reduced in memory space as opposed to remembering the value of every pixel.
Huffman's algorithm is an example of entropy-based compression. It derives a table based on the estimated probability or frequency of occurrence for each possible value of the source symbol. More common symbols are generally represented using fewer bits than less common symbols. Using binary nodes, the information is stored in an array. Each colour is associated with a certain node and encoded with the value associated with the node. Reconstruction of the image is produced by reading the string of nodes from left to right, top to bottom, to recreate the image.
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Dictionary-based compression are based around using a value-key system. Certain patterns of colours are assigned a value that is associated with a key. The string of key is a compressed version of the entire image. When decompressing, the image is rebuilt using the different keys and are piece together.
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