Presents an integrated system for navigation parameter estimation using sequential aerial images, where the navigation parameters represent the positional and velocity information of an aircraft for autonomous navigation. The proposed integrated system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current position of an aircraft by accumulating relative displacement estimates extracted from two successive aerial images. Simple accumulation of parameter values reduces the reliability of the extracted parameter estimates as an aircraft goes on navigating, resulting in a large positional error. Therefore, absolute position estimation is required to compensate for the positional error generated by the relative position estimation. Absolute position estimation algorithms using image matching and digital elevation model (DEM) matching are presented. In the image matching, a robust-oriented Hausdorff measure (ROHM) is employed, whereas in the DEM matching, an algorithm using multiple image pairs is used. Experiments with four real aerial image sequences show the effectiveness of the proposed integrated position estimation algorithm.

This paper proposes a motion compensation (MC) algorithm for robust transmission of video sequence, called the double-vector motion compensation (DMC). In the DMC, each block B in a frame is predicted from the weighted superposition of two blocks in the previous two frames, using two motion vectors. Therefore, when one of these two blocks is corrupted during the transmission, the decoder can efficiently suppress its error propagation to the subsequent frames, by predicting B only from the other block. It is shown by analysis that the DMC algorithm yields significantly lower error bounds for the subsequent frames than the conventional MC technique. Furthermore, the DMC algorithm can be combined with an effective concealment algorithm, which is capable of recovering very severe transmission errors, such as loss of an entire frame. A complete video coder, based on the DMC, is implemented by modifying the MC syntax of the H.263 standard and tested intensively in a realistic error prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the H.263 coder in the error-prone environment.

We propose an error-resilient transmission technique for the H.263 compatible video data stream, based on the data-partitioning technique. The proposed algorithm employs the bit rearrangement technique of the error-resilience entropy coding in each layer, providing unequal error protection against the channel errors, without requiring additional side information. In addition, we propose the recovery algorithm for the lost or erroneous motion vectors. The proposed algorithm is implemented, based on the H.263 standard, and evaluated through intensive computer simulation. The experimental results demonstrate that the proposed algorithm provides acceptable performance both subjectively and objectively at various bit error rates and burst lengths.

We propose a discrete cosine transform (DCT)-based post-processing technique to alleviate the blocking artifacts in transform coded images. In our approach, the high-frequency components, mainly caused by the edge components, are examined through two steps. First, two adjacent homogeneous blocks from the block boundary are found, where the homogeneous block is defined as the block in which no adjacent pixels' difference is larger than the difference of the block boundary. Second, the local frequency characteristics in the homogeneous block are examined through the DCT. Then, we derive the relation between the DCT coefficients of two homogeneous blocks of different sizes. By considering the information about the original edge and the relation, we can detect the high-frequency components, mainly caused by the blocking artifact. The performance of the proposed technique is evaluated on both still and moving images. The simulation results indicate that the proposed technique reduces the blocking artifacts effectively and preserves the original edges faithfully, and its performance is very robust to the degree of degradation in the decoded images.

The motion compensation-discrete cosine transform (MC-DCT) coding is an efficient compression technique for a digital video sequence. However, the compressed video signal is vulnerable to transmission errors over noisy channels. In this paper, we propose a robust video transmission algorithm, which protects the compressed video signal by inserting redundant information at the source level. The proposed algorithm encodes every lth frame in the semi-intra frame (S-frame) mode, in which the redundant parity-check DC coefficients (PDCs) are systematically inserted into the compressed bitstream. Then, the decoder is capable of recovering very severe transmission errors, such as loss of an entire frame, in addition to detecting the errors effectively without requesting any information from external devices. The proposed algorithm is implemented based on the H.263 coder, and tested intensively in realistic error prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the conventional H.263 coder in the error prone environment.

In this paper, a new contour-coding technique for object-oriented video coding is proposed, In our approach, the two-stage motion compensation technique is considered, in order to cope with the rather complex motion of the object. While the object-based motion compensation is performed in the first stage, the second-stage motion compensation is carried out to search the best match of the contours, which are not motion compensated in the first stage. Also by introducing the notion of the error band, the current contours can be properly fitted to the motion compensated contours using the interframe relationship of the contours. In addition, an efficient technique for encoding the contours, simplified by the majority filter, is also proposed. From the simulation result, it is shown that the proposed technique provides better performance than the content-based arithmetic encoding, especially when lossy encoding is allowed. Moreover, by varying the width N of the error band, it is shown that the bit amount for the shape information can be adjusted according to the channel condition.

Motion compensation-discrete cosine transform (MC-DCT) coding is an efficient compression technique for digital video sequences. However, the compressed video signal is vulnerable to transmission errors over noisy channels. In this paper, we introduce a novel concept of parity-check motion vector (PMV) into the MC-DCT coder in order to improve its error robustness. By inserting the redundant PMVs systematically into the compressed bitstream, the proposed algorithm is capable of recovering very severe transmission errors, such as loss of an entire frame, in addition to detecting the errors effectively without requesting any information from external devices. The proposed algorithm is implemented based on the H.263 coder, and tested intensively in a realistic error prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the conventional H.263 coder in the error prone environment.

This work presents a hybrid method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the position and velocity information of an aircraft for autonomous navigation. The proposed hybrid system is composed of two parts: relative position estimation and absolute position estimation. Computer simulation with two different sets of real aerial image sequences shows the effectiveness of the proposed hybrid parameter estimation algorithm.

We propose a novel postprocessing technique, based on the theory of projections onto convex sets (POCS), to reduce the blocking artifacts in transform coded images. It is assumed, in our approach, that the original image is highly correlated. Thus, the global frequency characteristics in two adjacent blocks are similar to the local ones in each block. We consider the high-frequency components in the global characteristics of a decoded image, which are not found in the local ones, as the results from the blocking artifact. We employ an N-point discrete cosine transform (DCT) to obtain the local characteristics, and a 2N-point DCT to obtain the global ones, and then derive the relation between the N-point and 2N-point DCT coefficients. A careful comparison of the N-point with the 2N-point DCT coefficients makes it possible to detect the undesired high-frequency components, mainly caused by the blocking artifact. Then, we propose novel convex sets and their projection operators in the DCT domain. The performance of the proposed and conventional techniques are compared on the still images, decoded by JPEG. The results show that, regardless of the content of the input images, the proposed technique yields significantly better performance than the conventional techniques in terms of objective quality, subjective quality, and convergence behavior.

We propose a novel algorithm for fractal video sequence coding, based on the circular prediction mapping and the noncontractive interframe mapping. The proposed algorithm can effectively exploit the temporal correlation in real image sequences, since each range block is approximated by the domain block in the adjacent frame, which is of the same size as the range block. The computer simulation results demonstrate that the proposed algorithm provides very promising performance at low bit rate, ranging from 40-250 kbyte/s.

We investigate the relation between VQ (vector quantization) and fractal image coding techniques, and propose a novel algorithm for still image coding, based on fractal vector quantization (FVQ). In FVQ, the source image is approximated coarsely by fixed basis blocks, and the codebook is self-trained from the coarsely approximated image, rather than from an outside training set or the source image itself. Therefore, FVQ is capable of eliminating the redundancy in the codebook without any side information, in addition to exploiting the self-similarity in real images effectively. The computer simulation results demonstrate that the proposed algorithm provides better peak signal-to-noise ratio (PSNR) performance than most other fractal-based coders.

This paper introduces a novel concept of PMV (parity motion vector) into the MC-DCT (motion compensation-discrete cosine transform) coder, in order to improve its robustness to transmission errors over noisy channels. By inserting the redundant PMVs systematically into the compressed bitstream, the proposed algorithm is capable of recovering very severe transmission errors, such as loss of an entire frame. The proposed algorithm is implemented based on the H.263 coder, and tested intensively in a realistic error prone environment. It is shown that the proposed algorithm provides much better objective and subjective performances than the conventional H.263 coder in the error prone environment.

This paper presents a method for navigation parameter estimation using sequential aerial images, where navigation parameters represent the velocity and position information of an aircraft for autonomous navigation. The proposed navigation parameter estimation system is composed of two parts: relative position estimation and absolute position estimation. Relative position estimation recursively computes the current velocity and position of an aircraft by accumulating navigation parameters extracted from two successive aerial images. However, simple accumulation of parameter values decreases reliability of the extracted parameters as an aircraft goes on navigating, resulting in a large position error. Therefore absolute position estimation is required to compensate for position error generated in the relative position estimation step. A hybrid absolute position estimation algorithm combining image matching and digital elevation model (DEM) matching is presented. In image matching, line segment matching or Hausdorff distance (HD) matching is employed whereas in DEM matching a new algorithm for absolute position estimation by minimizing the variance of displacements is proposed. Computer simulation with real aerial image sequences shows the effectiveness of the proposed algorithm.

We propose a novel fractal image compression technique, which does not require iteration at the decoder. The main problem relating to the conventional noniterative algorithm is that the smooth region cannot be coded efficiently, since the size of the range block is limited to be less than 8/spl times/8. We alleviate this problem by generating two codebooks from a planarly approximated image. In other words, the first codebook is generated by the smoothing operator for large and smooth range blocks and the second codebook is generated by the spatial contraction operator for small and active range blocks, respectively. The computer simulation results on the real images demonstrate that the proposed algorithm provides much better performance than most other fractal-based coders, in terms of the subjective quality as well as the objective quality (PSNR). Moreover, the proposed algorithm is very fast in decoding, since it does not require iteration at the decoder.