High dynamic range (HDR) content provides a better quality of experience than the standard dynamic range (SDR) content due to a wide luminance range, enhanced contrast, and saturated colors. Emotional experience analysis while watching SDR content has been an active research area in affective computing. However, the impact of HDR content on human emotional experience is not explored. This paper presents a statistical analysis of emotional experience in response to HDR and SDR content. To this end, SDR and HDR versions of four audio-visual clips are shown to two different groups, each comprising of 20 male and 10 female subjects. Each subject's emotional experience is recorded in terms of valence, arousal, and dominance scores after watching each clip. A t-test reveals that HDR and SDR content is statistically different in valence, arousal, and dominance scores for overall and gender-based analysis. The subject ratings show that the HDR content enhances the emotional experience in terms of valence and dominance scores.

Human stress is a serious health concern, which must be addressed with appropriate actions for a healthy society. This paper presents an experimental study to ascertain the appropriate phase, when electroencephalography (EEG) based data should be recorded for classification of perceived mental stress. The process involves data acquisition, pre-processing, feature extraction and selection, and classification. The stress level of each subject is recorded by using a standard perceived stress scale questionnaire, which is then used to label the EEG data. The data are divided into two (stressed and non-stressed) and three (non-stressed, mildly stressed, and stressed) classes. The EEG data of 28 participants are recorded using a commercially available four channel Muse EEG headband in two phases i.e., pre-activity and post-activity. Five feature groups, which include power spectral density, correlation, differential asymmetry, rational asymmetry, and power spectrum are extracted from five bands of each EEG channel. We propose a new feature selection algorithm, which selects features from appropriate EEG frequency band based on classification accuracy. Three classifiers i.e., support vector machine, the Naive Bayes, and multi-layer perceptron are used to classify stress level of the participants. It is evident from our results that EEG recording during the pre-activity phase is better for classifying the perceived stress. An accuracy of [Formula: see text] and [Formula: see text] is achieved for two- and three-class stress classification, respectively, while utilizing five groups of features from theta band. Our proposed feature selection algorithm is compared with existing algorithms and gives better classification results.

Here, a hybrid feature descriptor‐based method is proposed to recognise human emotions from their facial expressions. A combination of spatial bag of features (SBoFs) with spatial scale‐invariant feature transform (SBoF‐SSIFT), and SBoFs with spatial speeded up robust transform are utilised to improve the ability to recognise facial expressions. For classification of emotions, K ‐nearest neighbour and support vector machines (SVMs) with linear, polynomial, and radial basis function kernels are applied. SBoFs descriptor generates a fixed length feature vector for all sample images irrespective of their size. Spatial SIFT and SURF features are independent of scaling, rotation, translation, projective transforms, and partly to illumination changes. A modified form of bag of features (BoFs) is employed by involving feature's spatial information for facial emotion recognition. The proposed method differs from conventional methods that are used for simple object categorisation without using spatial information. Experiments have been performed on extended Cohn–Kanade (CK+) and Japanese female facial expression (JAFFE) data sets. SBoF‐SSIFT with SVM resulted in a recognition accuracy of 98.5% on CK+ and 98.3% on JAFFE data set. Images are resized through selective pre‐processing, thereby retaining only the information of interest and reducing computation time.

Scalable multiple description coding (SMDC) provides reliability and facility to truncate the descriptions according to the user rate-distortion requirements. In this paper we generalize the conditions of successive refinement of the side quantizer of a multiple description scalar quantizer that has overlapped quantizer cells generated by a modified linear index assignment matrix. We propose that the split or refinement factor for each of the refinement side quantizers should be greater than the maximum side quantizer bin spread and should not be integer multiples of each other for satisfying the SMDC distortion conditions and verify through simulation results on scalable multiple description image coding.

In this paper, we present a new method for scalable multiple description video coding based on motion compensated temporal filtering and multiple description scalar quantizer with successive refinement. In our method quality scalability is achieved by successively refining the side quantizers of a multiple description scalar quantizer. The rate of each description is allocated by considering different refinement levels for each spatio-temporal subband. The performance of the proposed scheme under lossless and lossy channel conditions are presented and compared with single scalable description video coding.

In this paper a new method for robust image transmission over lossy channels using distributed multiple description coding is presented. We propose an efficient yet a simple method for encoding each description from multiple description scalar quantizer by exploiting the correlation information among descriptions. In this way the correlated information which we call side information can be extracted at the decoder from each description. The motivation to such a type of encoding is to improve the side decoding and to increase the overall robustness of the scheme. Simulation results show that the side distortion is comparable with the central decoding distortion. Performance evaluation of the proposed approach over packet erasure channel shows 0.2-0.7 dB improvement compared to simple multiple description coding that uses non-distributed multiple description scalar quantizer and demonstrates the effectiveness of the scheme over lossy channels.

In this paper a novel scheme that incorporates both quality and resolution scalability within the multiple description coding (MDC) framework is presented. Different resolution descriptions are created by using several multiple description scalar quantizers (MDSQ) concept in different wavelet decomposition levels and the quality scalability is achieved in each description by successive refinement of the side quantizers to obtain enhancement layers. We propose the criteria to select different resolution descriptions from several MDSQs and the relationship between the refinement factor of each side quantizer of MDSQ and the number of diagonals filled in the MDSQ index assignment matrix. Rate distortion performances of the fully scalable image coding at different resolution and quality are presented. The Robustness of the proposed scheme is evaluated for the packet erasure channel and compared with scalable single description image coding.