Lossless compression

About: Lossless compression is a research topic. Over the lifetime, 13218 publications have been published within this topic receiving 199941 citations.

Data Compression Book

Abstract: From the Publisher: Topics in this guide to data compression techniques include the Shannon-Fano and Huffman coding techniques, Lossy compression, the JPEG compression algorithm, and fractal compression. Readers also study adaptive Huffman coding, arithmetic coding, dictionary compression methods, and learn to write C programs for nearly any environment. The disk illustrates each learned technique and demonstrates how data compression works.

A locally adaptive data compression scheme

Abstract: A data compression scheme that exploits locality of reference, such as occurs when words are used frequently over short intervals and then fall into long periods of disuse, is described. The scheme is based on a simple heuristic for self-organizing sequential search and on variable-length encodings of integers. We prove that it never performs much worse than Huffman coding and can perform substantially better; experiments on real files show that its performance is usually quite close to that of Huffman coding. Our scheme has many implementation advantages: it is simple, allows fast encoding and decoding, and requires only one pass over the data to be compressed (static Huffman coding takes two passes).

The Data Compression Book

Abstract: From the Publisher: Topics in this guide to data compression techniques include the Shannon-Fano and Huffman coding techniques, Lossy compression, the JPEG compression algorithm, and fractal compression. Readers also study adaptive Huffman coding, arithmetic coding, dictionary compression methods, and learn to write C programs for nearly any environment. The disk illustrates each learned technique and demonstrates how data compression works.

Lossless recovery of an original image containing embedded data

Abstract: The method and system embeds digital meta-data into an original image in such a way that the meta-data can be completely removed at a later time to allow loss less recovery of the original image. The loss less recovery of the original image allows for a digital signature of the image to be embedded in the image itself and later recovered and used to verify the authenticity of a received image.

Reversible data embedding into images using wavelet techniques and sorting

Abstract: The proliferation of digital information in our society has enticed a lot of research into data-embedding techniques that add information to digital content, like images, audio, and video. In this paper, we investigate high-capacity lossless data-embedding methods that allow one to embed large amounts of data into digital images (or video) in such a way that the original image can be reconstructed from the watermarked image. We present two new techniques: one based on least significant bit prediction and Sweldens' lifting scheme and another that is an improvement of Tian's technique of difference expansion. The new techniques are then compared with various existing embedding methods by looking at capacity-distortion behavior and capacity control.