Lossless compression of continuous-tone images via context selection, quantization, and modeling

Abstract

Context modeling is an extensively studied paradigm for lossless compression of continuous-tone images. However, without careful algorithm design, high-order Markovian modeling of continuous-tone images is too expensive in both computational time and space to be practical. Furthermore, the exponential growth of the number of modeling states in the order of a Markov model can quickly lead to the problem of context dilution; that is, an image may not have enough samples for good estimates of conditional probabilities associated with the modeling states. New techniques for context modeling of DPCM errors are introduced that can exploit context-dependent DPCM error structures to the benefit of compression. New algorithmic techniques of forming and quantizing modeling contexts are also developed to alleviate the problem of context dilution and reduce both time and space complexities. By innovative formation, quantization, and use of modeling contexts, the proposed lossless image coder has a highly competitive compression performance and yet remains practical.

Multi-resolution Lossless Image Compression for Progressive Transmission and Multiple Decoding Using an Enhanced Edge Adaptive Hierarchical Interpolation

“…Within a local context, the strength of the local error has strong correlation with the local variance of the original pixel values. Hence, the number of conditional entropy coding contexts can be reduced by representing each context using its error stength [29,30,31].The stength of error can be estimated using the local gradients which are pre-computed during the initial prediction. Except the top layer of the pyramid, each layer is scanned two times, and the interpolation is done at two stages of interpolation as described in detail in [10].…”

A High-Performance Lossless Compression Scheme for EEG Signals Using Wavelet Transform and Neural Network Predictors

“…Context-based error modeling has gained much research importance in improving the performance of compression algorithms [4, 22, 42, 43]. It is known that most state-of-the- art lossless coding techniques comprise prediction, context modeling of prediction error followed by entropy encoding [42].…”

“…Context modeling of prediction error is a means to separate these distributions thereby adjusting the offset to yield zero Laplacian distribution. Such scheme is referred to as context-based bias cancellation [4, 22, 42, 43] and has been applied to EEG signals using linear predictors [4] and neural network predictors [22]. In [4, 22] contexts were framed by computing the difference between the adjacent two samples.…”

“…The signal pattern is well exploited through the formation of contexts that help in better distribution of prediction errors [22, 23, 42, 43]. The application of appropriate encoder further decrease the bits required for transmitting the signal.…”

“…The application of appropriate encoder further decrease the bits required for transmitting the signal. In order to reduce the number of contexts, quantization of the d i values can be performed [42, 43]. A simple quantization is to obtain a two-level value based on certain threshold as shown in $Q\left(d_i\right)=\{\begin{array}{cc}left0,& left{d}_i<0\\ left1,& left{d}_i\ge 0\end{array}$ …”