Image coding using adaptive recursive interpolative DPCM with entropy-constrained trellis coded quantization.

Abstract

The goal of image coding is to represent images with a minimum amount of distortion at a given encoding rate. Image coding algorithms comprise methods for generating uncorrelated sequences and quantizing the uncorrelated sequences. The earliest encoding algorithms, such as Differential Pulse Code Modulation, are prediction based and must be considered primitive when compared to the more recent transform coders, such as Discrete Cosine Transform or Discrete Wavelet Transform. Judged only by SNR performance, the contemporary transform coders are far superior to the predictive coders. However, the computational complexity of the transform coders is much greater than predictive coders. In general, the improvement of hardware has diminished the importance of computational complexity. Thus, little research has been devoted to improving the performance of predictive coders. Furthermore, in a few applications such as remote decoding or real-time video decoding, the complexity of the decoder is still a constraint. In this dissertation, I have developed a predictive image coder having minimal decoder complexity and providing SNR's in the range of the most advanced transform coders. The image coder utilizes the Recursive Interpolative DPCM algorithm as a kernel in conjunction with an adaptive rate allocation scheme and entropy-constrained trellis coded quantization. The Adaptive RIDPCM-ECTCQ image coder is a high performance, low decoder-complexity alternative to contemporary transform coders.