Over the last decade, advancements in optical devices have made it possible for new novel image acquisition technologies to appear. Angular information for each spatial point is acquired in addition to the spatial information of the scene that enables 3D scene reconstruction and various post-processing effects. Current generation of plenoptic cameras spatially multiplex the angular information, which implies an increase in image resolution to retain the level of spatial information gathered by conventional cameras. In this work, the resulting plenoptic image is interpreted as a multi-view sequence that is efficiently compressed using the multi-view extension of high efficiency video coding (MV-HEVC). A novel twodimensional weighted prediction and rate allocation scheme is proposed to adopt the HEVC compression structure to the plenoptic image properties. The proposed coding approach is a response to ICIP 2017, Grand Challenge on Light Field Image Coding and the compression results are put in contrast to the state-of-art in plenoptic image compression presented at of the ICME 2016 Grand Challenge on Light-field Image Compression. The proposed scheme outperforms all contestants in the ICME Grand Challenge with a significant improvements in compression efficiency, i.e. with an average PSNR gain of 7.7 dB over reference JPEG image compression.

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Light field (LF) acquisition devices capture spatial and angular information of the scene. In contrast with traditional cameras, the additional angular information enables novel post-processing applications such as 3D scene reconstruction, refocusing at different depth planes, and synthetic aperture. In this paper, we present a novel compression scheme for LF data captured using multiple traditional cameras. The input LF views are divided into two groups, i.e. key views and decimated views. The key views are compressed using multiview extension of High Efficiency Video Coding (MV-HEVC) scheme and decimated views are predicted using the shearlet transform based prediction (STBP) scheme. Additionally, the residual information of predicted views is also encoded and sent along with the coded stream of key views. The proposed scheme is evaluated over benchmark multi-camera based LF dataset and it is demonstrated that incorporating the residual information into compression scheme increases the overall PSNR by 2 dB. The proposed compression scheme performs significantly better in low bit-rates compared to anchor schemes whose compression efficiency is better in high bit-rate scenarios. The sensitivity of the human vision system towards compression artifacts specifically in low bit-rates favors the proposed compression scheme over the anchor schemes.

The acquisition of the spatial and angular information of a scene using light field (LF) technologies supplement a wide range of post-processing applications, such as scene reconstruction, refocusing, virtual view synthesis, and so forth. The additional angular information possessed by LF data increases the size of the overall data captured while offering the same spatial resolution. The main contributor to the size of captured data (i.e., angular information) contains a high correlation that is exploited by state-of-the-art video encoders by treating the LF as a pseudo video sequence (PVS). The interpretation of LF as a single PVS restricts the encoding scheme to only utilize a single-dimensional angular correlation present in the LF data. In this paper, we present an LF compression framework that efficiently exploits the spatial and angular correlation using a multiview extension of high-efficiency video coding (MV-HEVC). The input LF views are converted into multiple PVSs and are organized hierarchically. The rate-allocation scheme takes into account the assigned organization of frames and distributes quality/bits among them accordingly. Subsequently, the reference picture selection scheme prioritizes the reference frames based on the assigned quality. The proposed compression scheme is evaluated by following the common test conditions set by JPEG Pleno. The proposed scheme performs 0.75 dB better compared to state-of-the-art compression schemes and 2.5 dB better compared to the x265-based JPEG Pleno anchor scheme. Moreover, an optimized motionsearch scheme is proposed in the framework that reduces the computational complexity (in terms of the sum of absolute difference [SAD] computations) of motion estimation by up to 87% with a negligible loss in visual quality (approximately 0.05 dB).INDEX TERMS Compression, light field, MV-HEVC, plenoptic.

Automated personal authentication has become increasingly important in modern information driven society and in this regard fingerprint-based personal identification is considered to be the most effective tool. In order to ensure reliable fingerprint identification and improve fingerprint ridge structure, a novel fingerprint enhancement approach is presented based on local adaptive contextual filtering. The proposed enhancement technique is 2-fold as it involves processing both in frequency and spatial domain. The fingerprint image is first filtered in frequency domain and then local directional filtering in spatial domain is applied to obtain enhanced fingerprint. In order to determine the performance efficiency of the proposed enhancement technique, a comparative analysis of error rates on standard fingerprint databases has been presented with major contextual enhancement schemes. The results show the efficacy of the proposed scheme as compared with other contextual filtering techniques.

Sentiment analysis is also known as opinion mining which shows the people's opinions and emotions about certain products or services. The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral. Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations. In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos. A comprehensive data set of five hundred thousand online reviews is used in our research. There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive). We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization. Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent. INDEX TERMS Sentiment, opinion mining, social media, natural language processing.

The experimentation was performed on two light field data sets: Stanford dataset and High Density Camera Array (HDCA) dataset. The rate-distortion analysis for the proposed compression scheme shows significant compression efficiency in low bit-rate scenarios as compared to the anchor compression scheme. However, the anchor performs better in high bit-rates. The sensitivity of human vision system towards the compression artifacts in low bit-rates favours the proposed compression scheme over the anchor. Figure 3: Rate Distortion analysis of proposed compression scheme with reference HEVC video compression standard on Stanford and HDCA LF images. ETN-FPI (Project number 676401) is funded under the H2020-MSCA-ITN-2015 call and is part of the Marie Sklodowska-Curie Actions-Innovative Training Networks (ITN) funding scheme