Manoranjan Paul

Just Noticeable Difference for Images With Decomposition Model for Separating Edge and Textured Regions

In just noticeable difference (JND) models, evaluation of contrast masking (CM) is a crucial step. More specifically, CM due to edge masking (EM) and texture masking (TM) needs to be distinguished due to the entropy masking property of the human visual system. However, TM is not estimated accurately in the existing JND models since they fail to distinguish TM from EM. In this letter, we propose an enhanced pixel domain JND model with a new algorithm for CM estimation. In our model, total-variation based image decomposition is used to decompose an image into structural image (i.e., cartoon like, piecewise smooth regions with sharp edges) and textural image for estimation of EM and TM, respectively. Compared with the existing models, the proposed one shows its advantages brought by the better EM and TM estimation. It has been also applied to noise shaping and visual distortion gauge, and favorable results are demonstrated by experiments on different images.

Direct Intermode Selection for H.264 Video Coding Using Phase Correlation

The H.264 video coding standard exhibits higher performance compared to the other existing standards such as H.263, MPEG-X. This improved performance is achieved mainly due to the multiple-mode motion estimation and compensation. Recent research tried to reduce the computational time using the predictive motion estimation, early zero motion vector detection, fast motion estimation, and fast mode decision, etc. These approaches reduce the computational time substantially, at the expense of degrading image quality and/or increase bitrates to a certain extent. In this paper, we use phase correlation to capture the motion information between the current and reference blocks and then devise an algorithm for direct motion estimation mode prediction, without excessive motion estimation. A bigger amount of computational time is reduced by the direct mode decision and exploitation of available motion vector information from phase correlation. The experimental results show that the proposed scheme outperforms the existing relevant fast algorithms, in terms of both operating efficiency and video coding quality. To be more specific, 82 ~ 92% of encoding time is saved compared to the exhaustive mode selection (against 58 ~ 74% in the relevant state-of-the-art), and this is achieved without jeopardizing image quality (in fact, there is some improvement over the exhaustive mode selection at mid to high bit rates) and for a wide range of videos and bitrates (another advantages over the relevant state-of-the-art).

Improved Gaussian mixtures for robust object detection by adaptive multi-background generation

Adaptive Gaussian mixtures are widely used to model the dynamic background for real-time object detection. Recently the convergence speed of this approach is improved and a relatively robust statistical framework is proposed by Lee (PAMI, 2005). However, object quality still remains unacceptable due to poor Gaussian mixture quality, susceptibility to background/foreground data proportion, and inability to handle intrinsic background motion. This paper proposes an effective technique to eliminate these drawbacks by modifying the new model induction logic and using intensity difference thresholding to detect objects from one or more believe-to-be backgrounds. Experimental results on two benchmark datasets confirm that the object quality of the proposed technique is superior to that of Leepsilas technique at any model learning rate.

Human detection in surveillance videos and its applications - a review

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.

Explore and Model Better I-Frames for Video Coding

In video coding, an intra (I)-frame is used as an anchor frame for referencing the subsequence frames, as well as error propagation prevention, indexing, and so on. To get better rate-distortion performance, a frame should have the following quality to be an ideal I-frame: the best similarity with the frames in a group of picture (GOP), so that when it is used as a reference frame for a frame in the GOP we need the least bits to achieve the desired image quality, minimize the temporal fluctuation of quality, and also maintain a more consistent bit count per frame. In this paper we use a most common frame of a scene in a video sequence with dynamic background modeling and then encode it to replace the conventional I-frame. The extensive experimental results confirm the superiority of our proposed scheme in comparison with the existing state-of-art methods by significant image quality improvement and computational time reduction.

Epileptic seizure detection by analyzing EEG signals using different transformation techniques

Abstract Feature extraction and classification are still challenging tasks to detect ictal (i.e., seizure period) and interictal (i.e., period between seizures) EEG signals for the treatment and precaution of the epileptic seizure patient due to different stimuli and brain locations. Existing seizure and non-seizure feature extraction and classification techniques are not good enough for the classification of ictal and interictal EEG signals considering for their non-abruptness phenomena, inconsistency in different brain locations, type (general/partial) of seizures, and hospital settings. In this paper we present generic seizure detection approaches for feature extraction of ictal and interictal signals using various established transformations and decompositions. We extract a number of statistical features using novel ways from high frequency coefficients of the transformed/decomposed signals. The least square support vector machine is applied on the features for classifications. Results demonstrate that the proposed methods outperform the existing state-of-the-art methods in terms of classification accuracy, sensitivity, and specificity with greater consistence for the large size benchmark dataset in different brain locations.

An Efficient Mode Selection Prior to the Actual Encoding for H.264/AVC Encoder

Many video compression algorithms require decisions to be made to select between different coding modes. In the case of H.264, this includes decisions about whether or not motion compensation is used, and the block size to be used for motion compensation. It has been proposed that constrained optimization techniques, such as the method of Lagrange multipliers, can be used to trade off between the quality of the compressed video and the bit rate generated. In this paper, we show that in many cases of practical interest, very similar results can be achieved with much simpler optimizations. Mode selection by simply minimizing the distortion with motion vectors and header information produces very similar performance to the full constrained optimization, while it reduces the mode selection and over all encoding time by 31% and 12%, respectively. The proposed approach can be applied together with fast motion search algorithms and the mode filtering algorithms for further speed up.

A real-time pattern selection algorithm for very low bit-rate video coding using relevance and similarity metrics

Very low bit-rate video coding using regularly shaped patterns to represent moving regions in macroblocks has good potential for improved coding efficiency. This paper presents a real-time pattern selection (RTPS) algorithm, which uses a pattern relevance and similarity metric to achieve faster pattern selection from a large codebook. For each applicable macroblock, the relevance metric is applied to create a customized pattern codebook (CPC) from which the best pattern is selected using the similarity metric. The CPC size is adapted to facilitate real-time selection. Results prove the quantitative and perceptual performance of RTPS is superior to both the Fixed-8 algorithm and H.263.

Video Coding Focusing on Block Partitioning and Occlusion

Among the existing block partitioning schemes, the pattern-based video coding (PVC) has already established its superiority at low bit-rate. Its innovative segmentation process with regular-shaped pattern templates is very fast as it avoids handling the exact shape of the moving objects. It also judiciously encodes the pattern-uncovered background segments capturing high level of interblock temporal redundancy without any motion compensation, which is favoured by the rate-distortion optimizer at low bit-rates. The existing PVC technique, however, uses a number of content-sensitive thresholds and thus setting them to any predefined values risks ignoring some of the macroblocks that would otherwise be encoded with patterns. Furthermore, occluded background can potentially degrade the performance of this technique. In this paper, a robust PVC scheme is proposed by removing all the content-sensitive thresholds, introducing a new similarity metric, considering multiple top-ranked patterns by the rate-distortion optimizer, and refining the Lagrangian multiplier of the H.264 standard for efficient embedding. A novel pattern-based residual encoding approach is also integrated to address the occlusion issue. Once embedded into the H.264 Baseline profile, the proposed PVC scheme improves the image quality perceptually significantly by at least 0.5 dB in low bit-rate video coding applications. A similar trend is observed for moderate to high bit-rate applications when the proposed scheme replaces the bi-directional predictive mode in the H.264 High profile.

Video coding using the most common frame in scene

Motion estimation (ME) and motion compensation (MC) using variable block size, fractional search, and multiple reference frames (MRFs) help the recent video coding standard H.264 to improve the coding performance significantly over the other contemporary coding standards. The concept of MRF achieves better coding performance in the cases of repetitive motion, uncovered background, non-integer pixel displacement, lighting change, etc. The requirement of index codes of the reference frames, computational time in ME&MC, and memory buffer for pre-coded frames limits the number of reference frames used in practical applications. In typical video sequence, the previous frame is used as a reference frame with 68∼92% of cases. In this paper, we propose a new video coding method using a reference frame (i.e., the most common frame in scene (McFIS)) generated by the Gaussian mixture based dynamic background modelling. The McFIS is not only more effective in terms of rate-distortion and computational time performance compared to the MRFs but also error resilient transmission channel. The experimental results show that the proposed coding scheme outperforms the H.264 standard video coding with five reference frames by at least 0.5 dB and reduced 60% of computation time.