This document discusses different types of image compression including lossless and lossy compression. Lossless compression reduces file size without degrading image quality, while lossy compression removes some image data. The document also mentions model compression techniques for neural networks, including pruning, quantization, and Huffman coding. Specific lossless compression algorithms are listed such as CCITT group 3 & 4, Flate/deflate, Huffman, LZW, and RLE compression. Entropy encoding is also discussed as a final lossless compression step done after image quantization.
3. Image compression is a process applied to a
graphics file to minimize its size in bytes
without degrading image quality below an
acceptable threshold. By reducing the file
size, more images can be stored in a given
amount of disk or memory space.
4. The Types Of Image Compression? Image
compression has two prime categories -
lossless and lossy image compression. These
vary based on the image file resizing process.
While the former ensures the image quality
remains intact, the latter removes some parts
to get a smaller size.
5. Model compression aims to alleviate the costs
of large model sizes (like the ones mentioned
above) by representing the model in a more
efficient format with minimal impact on its
performance. In recent research, three
methods have emerged as especially
important (and interesting) strategies for
model compression.
6. It takes up less space on the hard drive and
retains the same physical size, unless edit the
image's physical size in an image editor. The
file size reduction with the help of internet, to
create image rich sites without using much
bandwidth or storage space.
8. Compression algorithms are normally used to
reduce the size of a file without removing
information. This can increase their entropy
and make the files appear more random
because all of the possible bytes become
more common.
9. Entropy encoding which is a way of lossless
compression that is done on an image after
the quantization stage. It enables to
represent an image in a more efficient way
with smallest memory for storage or
transmission.
