An Advanced Image Coding Algorithm that Utilizes Shape-Adaptive DCT for Providing Access to Content

Shape-Adaptive Discrete Cosine Transform (SA-DCT) has been proposed for coding arbitrarily shaped objects. SA-DCT was used in lieu of DCT, and suitable modifications were made to the MPEG- 2 codec to provide access to objects comprising an image. It was established that this additional functionality was not obtained at the cost of compression efficiency. SA-DCT along with contour coding, using chain-difference method, was verified to be an efficient method for coding arbitrairly shaped objects by performing experiments with standard test images. This paper has previously appeared as a Technical Report at Philips Research. </ul

A Convex Model for the Robust Estimation of Optical Flow for, Motion-Based Image Segmentation

The goal of motion-based segmentation is to partition the image into regions that have different characteristics or properties. The paper establishes feasibility of using computer vision algorithms for real-time segmentation and compression of motion video sequences. A convex formulation, using Huber's regularizer, in a robust estimation framework has significant advantages over previous approaches. Unlike previous techniques, our approach guarantees stable, repeatable (or reproducible) segmentations which make real-time applications in segmenting video possible. <BR

Scalable Coding of Video Objects

This paper provides a methodology to encode video objects in a scalable manner with regard to both content and quality. Content scalability and quality scalability have been identified as required features in order to support video coding across different environments. Following the object-based approach to coding video, we extend our previous work on motion-based segmentation by using a time recursive approach to segmenting image sequences and decomposing a video "shot" into its constituent objects. Our formulation of the segmentation problem enables us to design a codec in which the information (shape, texture and motion) pertaining to each video object is encoded independently of the other. The multiresolution wavelet decomposition used in encoding texture information is shown to be helpful in providing spatial scalability. Our codec design is also shown to be temporally scalable. This report was accepted for oral presentation at the IEEE International Symposium on Circuits & Systems, Monterey, Calif., May-June 1998

Accurate Segmentation and Estimation of Parametric Motion Fields for Object-based Video Coding using Mean Field Theory

We formulate the problem of decomposing a scene into its constituent objects as one of partitioning the current frame into objects comprising it. The motion parameter is modeled as a nonrandom but unknown quantity and the problem is posed as one of Maximum Likelihood (ML) estimation. The MRF potentials which characterize the underlying segmentation field are defined in a way that the spatio-temporal segmentation is constrained by the static image segmentation of the current frame. To compute the motion parameter vector and the segmentation simultaneously we use the Expectation Maximization (EM) algorithm. The E-step of the EM algorithm, which computes the conditional expectation of the segmentation field, now reflects interdependencies more accurately because of neighborhood interactions. We take recourse to Mean Field theory to compute the expected value of the conditional MRF. Robust M-estimation methods are used in the M- step. To allow for motions of large magnitudes image frames are represented at various scales and the EM procedure is embedded in a hierarchical coarse-to-fine framework. Our formulation results in a highly parallel algorithm that computes robust and accurate segmentations as well as motion vectors for use in low bit rate video coding. This report has been submitted as a paper to the SPIE conference on Visual Communications and Image Processing - VCIP98 to be held in San Jose, California on Jan 24- 30, 1998. </Center