One of the challenging problems that most existing video codecs face today is the encoding of the information pertaining to the occluded areas, i.e., the areas which are covered or uncovered by moving objects. The existing techniques necessitate the transmission of the position information of the occluded areas following the detection process, which can constitute a large overhead in bandwidth consumption. In addition, these detection techniques fail under noisy conditions. On the other hand, no effort was made to incorporate the spatio-temporal correlation that exists between motion fields of consecutive frames. A new method to detect the occluded areas is described. In this method, the decoder is given additional intelligence to extract the position information of the occluded areas, thus reducing the bandwidth needed to transmit the occlusion information significantly. According to the proposed method, the temporal correlation of the motion fields is exploited. The proposed method is robust under noisy conditions and provides a computationally simple solution.

Image and video coding algorithms have found a number of applications ranging from video telephony on the public switched telephone networks (PSTN) to HDTV. However, as the bit rate is lowered, most of the existing techniques, as well as current standards, such as JPEG, H. 261, and MPEG-1 produce highly visible degradations in the reconstructed images primarily due to the information loss caused by the quantization process. In this paper, we propose an iterative technique to reduce the unwanted degradations, such as blocking and mosquito artifacts while keeping the necessary detail present in the original image. The proposed technique makes use of a priori information about the original image through a nonstationary Gauss-Markov model. Utilizing this model, a maximum a posteriori (MAP) estimate is obtained iteratively using mean field annealing. The fidelity to the data is preserved by projecting the image onto a constraint set defined by the quantizer at each iteration. The proposed solution represents an implementation of a paradigm we advocate, according to which the decoder is not simply undoing the operations performed by the encoder, but instead it solves an estimation problem based on the available bitstream and any prior knowledge about the source image. The performance of the proposed algorithm was tested on a JPEG, as well as on an H.261-type video codec. It is shown to be effective in removing the coding artifacts present in low bit rate compression.>

Most of the transform-based image compression techniques produce visible artifacts in the reconstructed image, particularly at low bit rates. In this paper, we propose an iterative technique to reduce the unwanted degradations such as blocking artifacts while keeping the necessary detail present in the original image. The proposed technique makes use of a priori information about the original image by using a nonstationary Gauss-Markov model. A MAP estimate is obtained iteratively using mean field annealing. A additional a priori information about the transform of the original image is incorporated into the estimation process by projecting the image onto a set defined by the quantizer at each iteration. The performance of the proposed algorithm was tested on JPEG compressed images. It is shown to be effective in removing the coding artifacts present in the low bit rate compressed images.>

In this paper a new motion compensated (MC) predictive coding method for image sequences at very low bit rates is presented. This method utilizes a prediction of the displacement vector field (DVF) in order to produce an MC prediction error which is coded and transmitted. Assuming that the two previous frames are available both at the transmitter and the receiver the DVF corresponding to the previous frame is estimated. Then, based on this estimate a temporal prediction of the DVF at the current frame is obtained. Using the assumption that the motion is constant along its trajectory, an auto-regressive (AR) model based method for performing such a prediction is proposed. According to the proposed scheme there is no need to transmit the DVF. Since the transmission of the DVF represents a sizeable overhead in very low bit rate coding of video signals, this method constitutes a major contribution to bit rate reduction and quality improvement. Using the predicted DVF, the MC prediction error is obtained, which is then transform and entropy coded. The proposed algorithm is experimentally tested on standard video-conferencing image sequences and compared to previously reported methods which transmit the motion vectors. Significantly improved results are obtained compared to the methods that transmit the motion vectors in terms of reduced bit- rate and improved quality of the reconstructed image sequence.