Coding algorithm for grayscale images based on Linear Prediction and dual mode quantization

This paper proposes a novel algorithm for grayscale image compression based on dual mode quantization that is supported by improved linear prediction scheme. The idea of dual mode quantization comes from desire to exploit advantages of the both uniform and piecewise uniform quantizers, designed for discrete input samples. The algorithm performs quantizers with a low and medium number of quantization levels and with a fixed codeword length by using a pixel value prediction in preprocessing. The correlation of adjacent pixels is exploited as the main idea for improving the quality of image compression. The proposed prediction is linear and very simple for practical realization. An analysis of reconstructed image quality is presented considering several parameters and by comparing with few other methods - BTC, DPCM and with methods that use transformation coding. Experiments are done applying the proposed compression model to several standard grayscale test images. Special attention is given to determination of thresholds values that determine whether and which of the two offered quantizers to use. Moreover, method for determining the value of proposed quantizer's variance is explained. Obtained results show that proposed model ensures gain up to 6.14 [dB] compared to the BTC model that uses fixed piecewise uniform quantization for discrete input without a pixel value prediction as well as gain up to 5.89 [dB] compared to the DPCM model that applies dual predictor. The proposed algorithm could find application in current grayscale image compression and video standards.