A new approach to texture classification is described which is based on measurements of the spatial gray-level co-occurrence probability matrix. This approach can make use of assumed stochastic models for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The efficacy of the approach is demonstrated through experimental results obtained with real-world texture data.

A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in realworld imagery are discussed.

An approach is described for exploiting the tradeoffs between source and channel coding in the context of image transmission. The source encoder employs two-dimensional (2-D) block transform coding using the discrete cosine transform (DCT). This technique has proven to be an efficient and readily implementable source coding technique in the absence of channel errors. In the presence of channel errors, however, the performance degrades rapidly, requiring some form of error-control protection if high quality image reconstruction is to be achieved. This channel coding can be extremely wasteful of channel bandwidth if not applied judiciously. The approach described here provides a rationale for combined source-channel coding which provides improved quality image reconstruction without sacrificing transmission bandwidth. This approach is shown to result in a relatively robust design which is reasonably insensitive to channel errors and yet provides performance approaching theoretical performance limits. Analytical results are provided for assumed 2-D autoregressive image models, while simulation results are provided for real-world images.