An approach is based on the block discrete cosine transform (DCT). The novelty of this approach is that the transform coefficients of all image blocks are coded and transmitted in absolute magnitude order. The resulting ordered-by-magnitude transmission is accomplished without sacrificing coding efficiency by using partition priority coding. Coding and transmission are adaptive to the characteristics of each individual image. and therefore, very efficient. Another advantage of this approach is its high progression effectiveness. Since the largest transform coefficients that capture the most important characteristics of images are coded and transmitted first, this method is well suited for progressive image transmission. Further compression of the image-data is achieved by multiple distribution entropy coding, a technique based on arithmetic coding. Experiments show that the approach compares favorably with previously reported DCT and subband image codecs.

A new DCT (discrete cosine transform) based coding method for image sequences is presented. This technique utilizes the between frame difference of the DCT coefficients which are coded and transmitted progressively according to their magnitude (large values first). This is achieved without any overhead for the position information of the ordered samples using priority progressive coding (PPC). PPC is a new method that allows the ordered transmission of data sources without any overhead for the position information. Experiments were performed to test this method. The results from these experiments show improvement over previously reported DCT based methods.>

Exceptional conditions are anomalous data which meet the intent of a schema but not the schema definition, represent a small proportion of the database extension, and may become known only after the schema is in use. Admission of exceptional conditions is argued to suggest a representation that locally stretches the schema definition by use of relations with embedded subrelations. Attempted normalization of these relations to 1NF does not yield the static schema typically associated with such transformations. A class of relations, termed Exceptional Condition Nested Form (ECNF), is defined which allows the necessary representation of exceptional conditions while containing sufficient restrictions to prevent arbitrary and chaotic inclusion of embedded subrelations. Queries on a subset of exceptional conditions, the exceptional constraints , are provided an interpretation via an algorithm that transforms ECNF relations into 1NF relations containing two types of null values. Extensions of relational algebraic operators, suitable for interactive query navigation, are defined for use with ECNF relations containing all forms of exceptional conditions.

A lossless progressive transmission method for grey-scale images which concentrates early transmission efforts on areas of greater image information content is described. The receiver does not have a priori knowledge of which image areas are to receive preferential treatment, and the preferential level of resolution is the pixel. The method makes use of simultaneous geometric and information content decompositions. The method is computationally simple with a complexity which grows linearly with the number of pixels. Compression achieved approaches that obtained by nonprogressive lossless methods, and is approximately the same as for homogeneous progressive lossless methods. Extensions of the method for progressive transmission with limited distortion and greater compression are also discussed.