TensorFlow Compression (TFC) contains data compression tools for TensorFlow. You can use this library to build your own ML models with end-to-end optimized data compression built in. It's useful to ...

Abstract: Context-based adaptive binary arithmetic coding (CABAC) as a normative part of the new ITU-T/ISO/IEC standard H.264/AVC for video compression is presented. By combining an adaptive binary ...

We build a state model of the DC coefficients of the entire image. On each 8x8 block in the image, we perform DCT, quantization, and run length encoding on the resulting coefficients. Normally ...

Lossless data compression is an essential technology that allows the compressed data to be perfectly reconstructed. It is indispensable for executable programs, text documents, genomics, cryptography ...

Abstract: A new entropy coding scheme for video compression is presented. Context models are utilized for efficient prediction of the coding symbols. A novel binary adaptive arithmetic coding ...

We consider the attributes of a point cloud as samples of a vector-valued volumetric function at discrete positions. To compress the attributes given the positions, we compress the parameters of the ...

Resources are always limited. Whether storage space or communication bandwidth, is not usually sufficient, which inspires us to apply compression that aims to reduce the number of bits needed to ...

Robert Granger holds an MSci from the School of Mathematics, University of Bristol (2002) and a PhD from the Department of Computer Science, University of Bristol (2006). He has held postdoctoral ...

ABSTRACT: This paper proposes a Full Range Gaussian Markov Random Field (FRGMRF) model for monochrome image compression, where images are assumed to be Gaussian Markov Random Field. The parameters of ...

ABSTRACT: This paper presents a new method of lossless image compression. An image is characterized by homogeneous parts. The bit planes, which are of high weight are characterized by sequences of 0 ...