Context Modeling and Correction of Quantization Errors in Prediction Loop

In lossy predictive coding of Differential Pulse Code Modulation (DPCM) type, quantization performed in the prediction loop induces propagation of quantization errors, resulting in biased predictions of the subsequent samples. In this work, we aim to alleviate the negative effect of quantization errors on the robustness of prediction. We propose some practical techniques for context modeling of quantization errors and cancelation of estimation biases in the DPCM reconstruction. The resulting refined estimates are fed into the prediction to improve coding efficiency. When applied to ID audio and 2D image signals, the proposed techniques can reduce the bit rate and at the same time improve the PSNR performance significantly.