LIMITED MOTION ESTIMATION SCHEME FOR MULTIMEDIA VIDEO COMPRESSION

ABSTRACT This paper presents a computationally efficient molinn estimation technique based on image sampling which determines the dominant motion between pairs of images. The technique is suitcd to low complexity, low bit rate multimedia applications, where the objective is to achieve good fidelity wilhout the overhead o i full motion compensation. This can he achieved if the dominant motion is a combination of translation, rotation and zoom, which can be described by a similarity transformation. The method adopts a new approach to determining the model parameters, based on gcneraling ii list 01' parameter estimates from pairs of block motion vectors and selecling the mean uf those estimates cluse lo the median. The method gives a good sub-pixel dominant motion estimate by sampling as little as 1/20"' of the image area. itesults show the method to be accurate and robust, with low coinpulational rcquiremcnts

Localized temporal decorrelation for video compression

Many of the current video compression algorithms perform analysis and coding operations in a block-wise manner. Most of them use a motion compensated DCT algorithm as the basis. Many other codecs, mostly academic and in their infancy and known as Second Generation techniques, utilize region and contour based and model based techniques. Unfortunately, these second-generation methods have not been successful in gaining widespread acceptance in both the standards and the consumer world. Many of them require specialized computationally intensive software and/or hardware. Due to these shortcomings, current block based methods have been finetuned to get better performance at even very low bit rates (sub 64 kbps). Block based motion estimation is the principal mechanism used to compensate for motion between frames in an image sequence. Although current algorithms are fast and quite effective, they fail in compensating for uncovered background areas in a frame. Solutions such as hierarchical motion estimation schemes do not work very well since there is no reference in past, and in some cases, future frames for an uncovered background resulting in the block being transmitted as an intra frame (which requires the most bandwidth among all type of blocks). This thesis intro duces an intermediate stage, which compensates for these isolated uncovered areas. The intermediate stage uses a localized decorrelation technique to reduce frame to frame temporal redundancies. The algorithm can be easily incorporated into exist ing systems to achieve an even better performance and can be easily extended as a scalable video coding architecture. Experimental results show that the algorithm, used in conjunction with motion estimation, is quite effective in reducing temporal redundancies

Source Camera Verification from Strongly Stabilized Videos

Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires the inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level, incorporates a number of constraints to validate their correctness, and offers computational flexibility in the search for the correct transformation. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that the proposed method is able to verify the source of 23-30% of all videos that underwent stronger stabilization, depending on computation load, without a significant impact on false attribution

Real Time Motion Estimation Algorithm for Temporal Denoising

This thesis introduces a low-complexity, but efficient, motion estimation algorithm, that could be implemented in FPGA, in a professional digital camera to apply it on-the-fly while recording a video-sequence.The main aim of the proposed algorithm it to improve the performance of an already existing denoising algorithm. To meet the real-time constraint, the prediction accuracy is traded for a reduced number of operations that is reflected in a faster computational time