Moyuresh Biswas

Multiple Description Wavelet Video Coding Employing a New Tree Structure

We describe a multiple description (MD) video coding technique using 3D-set partitioning in hierarchical tree algorithm (3D-SPIHT). Multiple description coding (MDC) is a way of coding video information into multiple bitstreams and is useful in a number of scenarios including peer-to-peer video streaming and video transmission over error-prone networks. We propose a modified tree structure for 3D-SPIHT that is more efficient for MD coding. We also propose a branch-pruning technique to generate multiple descriptions. We then show how rate-distortion optimization can be incorporated into the MDC framework to develop an efficient MD video coding system. Experimental results show that the proposed MD system achieves significant improvement in performance compared to existing methods.

Dense depth estimation using adaptive structured light and cooperative algorithm

In this paper we propose a new depth estimation approach using adaptive structured light. A random noise adaptive structured light pattern is projected onto objects and then two cameras capture stereo images. The adaptive colors are acquired using principle component analysis in the RGB color space of the image of the scene. By using inverse principle component analysis on the images with structured light, the desirable structured light information can be maximally retrieved. By combining the original three RGB channels of the scene under adaptive structured light with a fourth channel generated using inverse principle component analysis we can use the cooperative algorithm to generate a dense depth map. In order to keep clear depth discontinuities and alleviate noise in the depth map, we aggregate the local match score with shiftable windows. Experimental results show our approach performs well on images of real-world objects with strong colors and complex textures that have been captured under ambient light conditions.

Accurate depth estimation using structured light and passive stereo disparity estimation

In this paper we propose a new approach to depth estimation which combines structured light and passive stereo disparity estimation techniques to generate accurate disparity maps in both textured and textureless areas of a scene. We project a structured light pattern with adaptive colors onto the scene and simultaneously capture stereo images with a pair of cameras. By matching points in the projected pattern and the left image we first acquire a sparse disparity map. This sparse disparity map is interpolated and used to initialize a passive stereo disparity algorithm to improve disparity accuracy in textured areas. Finally, this map is interpolated in the textureless areas. By comparing our final map with efficient belief propagation and the initial interpolated disparity map, we show our approach performs better than using passive-only or active-only techniques.

A Low-Complexity Image Registration Algorithm for Global Motion Estimation

An important recent application of image registration is the estimation of global motion parameters for object-based video coding. However, the main disadvantage of standard approaches to global motion estimation (GME) is the increased computational complexity with the higher degree of motion models when compared to block-based local motion estimation approaches. In this paper, we propose a new low complexity GME algorithm. In our proposed algorithm, full-precision images are replaced with 1 bit-per-pixel images which allows many of the arithmetic operations in the standard GME approach to be replaced with logic operations. Experimental results show that our proposed algorithm achieves the same registration accuracy as the standard GME approach but with significantly reduced computational complexity. Our results also demonstrate the superior performance of the proposed algorithm when compared with previously proposed low-complexity GME approaches.

Improved Resilience for Video Over Packet Loss Networks With MDC and Optimized Packetization

We investigate the problem of robust video transmission over packet loss networks. A resilient video coding framework is important to ensure quality video over these networks. We propose a combination of multiple description coding and an optimized packetization method to constitute the error resilient codec. Multiple description coding technique utilizes path-diversity (multiple paths between sender and receiver) in networks by sending the descriptions along different paths. An optimized strategy for packetizing the descriptions is also proposed which guarantees that each packet is self-contained and efficient. Experimental results show that the proposed method achieves improved video quality over lossy networks.

Improved resilience for video over packet loss networks with MDC and optimized packetization

We investigate the problem of robust video transmission over lossy packet networks. A resilient video coding framework is important to ensure quality video over these networks. We propose a combination of a rate-distortion optimized multiple description codec and an integrated packetization method to constitute the error resilient codec. Multiple description (MD) coding techniques utilize path-diversity (multiple paths between sender and receiver) in networks by sending the descriptions along different paths. Two different optimization controls for the MD codec are proposed that are suited to variable rates of packet loss for multipath transmission of the MD coded video. An optimized strategy for packetizing the descriptions is also proposed which guarantees that each packet is self-contained and efficient. Simulations done under various packet loss scenarios show the need for two different optimization strategies and also that the developed MD codec achieves significantly improved video quality when compared with similar techniques.

Improved H.264-based video coding using an adaptive transform

In block-based video coding, the Discrete Cosine Transform (DCT) has been adopted for signal decorrelation in state-of-the-art standards. Although the Karhunen Loeve Transform (KLT) is known to achieve optimal energy compaction, it has been reported to offer only moderate compression as the KLT basis functions are source dependent and hence require the transform itself to be coded. This paper describes a technique for prediction-error block coding using the KLT. The proposed method does not require coding of the KLT bases. Instead the basis functions can be derived at the decoder in a manner similar to the encoder. The proposed method is incorporated into a standard H.264 video codec using an adaptive transform selection approach. Our experiments show that the Peak Signal-to-Noise Ratio (PSNR) improvement of up to 0.9 dB is achieved with the proposed technique when compared with the standard H.264 codec.

Multiple Description Video Codingwith 3D-Spiht Employing a New Tree Structure

Multiple description coding (MDC) of video is a useful technique for robust video transmission over unreliable networks. MDC not only provides at least acceptable quality of video in error-prone network but it also efficiently utilizes the multipath nature of Internet and wireless networks. In this paper, multiple description video coding with 3-D set partitioning in hierarchical tree (3D-SPIHT) codec has been proposed. In particular, we propose branch-pruning technique to generate multiple descriptions. We then propose a new tree structure for 3D-SPIHT which is particularly efficient for MDC. Experimental results prove the effectiveness of the proposed method.

A Slice Based Technique for Low-Complexity 3D/2D Registration of CT to Single Plane X-Ray Fluoroscopy

The registration of 3D CT volumes to 2D fluoroscopy video frames has been used in a number of important medical applications such as image guided surgery and kinematic analysis of joints. A major limitation for this type of registration is the computational complexity of the algorithms required to perform the registration. These algorithms present a significant computational burden since they are iterative in nature and a new 3D CT volume must be calculated at every iteration. In this paper we propose a new fast single-plane 3D/2D image registration algorithm which only updates partial 3D data for some of the iterations instead of updating the full 3D volume at every iteration. Experimental results show that our algorithm can perform the 3D/2D registration 10 times faster than conventional approaches with no significant loss of accuracy or reduction in successful registration rate.

An adaptive low-complexity global motion estimation algorithm

One important recent application of image registration has been in the estimation of global motion parameters for object-based video coding. A limitation of current global motion estimation approaches is the additional complexity of the gradient-descent optimization that is typically required to calculate the optimal set of global motion parameters. In this paper we propose a new low-complexity algorithm for global motion estimation. The complexity of the proposed algorithm is reduced by performing the majority of the operations in the gradient-descent optimization using logic operations rather than full-precision arithmetic operations. This use of logic operations means that the algorithm can be implemented much more easily in hardware platforms such as field programmable gate arrays (FPGAs). Experimental results show that the execution time for software implementations of the new algorithm is reduced by a factor of almost four when compared to existing fast implementations without any significant loss in registration accuracy.