Mid Sweden University

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 two dimensional 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: Light field Image Coding. The proposed scheme outperforms all ICME contestants, and improves on the JPEG-anchor of ICME with an average PSNR gain of 7.5 dB and the HEVC-anchor of ICIP 2017 Grand Challenge with an average PSNR gain of 2.4 dB.

With the advent of light field acquisition technologies, the captured information of the scene is enriched by having both angular and spatial information. The captured information provides additional capabilities in the post processing stage, e.g. refocusing, 3D scene reconstruction, synthetic aperture etc. Light field capturing devices are classified in two categories. In the first category, a single plenoptic camera is used to capture a densely sampled light field, and in second category, multiple traditional cameras are used to capture a sparsely sampled light field. In both cases, the size of captured data increases with the additional angular information. The recent call for proposal related to compression of light field data by JPEG, also called “JPEG Pleno”, reflects the need of a new and efficient light field compression solution. In this paper, we propose a compression solution for sparsely sampled light field data. In a multi-camera system, each view depicts the scene from a single perspective. We propose to interpret each single view as a frame of pseudo video sequence. In this way, complete MxN views of multi-camera system are treated as M pseudo video sequences, where each pseudo video sequence contains N frames. The central pseudo video sequence is taken as base View and first frame in all the pseudo video sequences is taken as base Picture Order Count (POC). The frame contained in base view and base POC is labeled as base frame. The remaining frames are divided into three predictor levels. Frames placed in each successive level can take prediction from previously encoded frames. However, the frames assigned with last prediction level are not used for prediction of other frames. Moreover, the rate-allocation for each frame is performed by taking into account its predictor level, its frame distance and view wise decoding distance relative to the base frame. The multi-view extension of high efficiency video coding (MV-HEVC) is used to compress the pseudo multi-view sequences. The MV-HEVC compression standard enables the frames to take prediction in both direction (horizontal and vertical d), and MV-HEVC parameters are used to implement the proposed 2D prediction and rate allocation scheme. A subset of four light field images from Stanford dataset are compressed, using the proposed compression scheme on four bitrates in order to cover the low to high bit-rates scenarios. The comparison is made with state-of-art reference encoder HEVC and its real-time implementation X265. The 17x17 grid is converted into a single pseudo sequence of 289 frames by following the order explained in JPEG Pleno call for proposal and given as input to the both reference schemes. The rate distortion analysis shows that the proposed compression scheme outperforms both reference schemes in all tested bitrate scenarios for all test images. The average BD-PSNR gain is 1.36 dB over HEVC and 2.15 dB over X265.

Light field (LF) acquisition technologies capture the spatial and angular information present in scenes. The angular information paves the way for various post-processing applications such as scene reconstruction, refocusing, and synthetic aperture. The light field is usually captured by a single plenoptic camera or by multiple traditional cameras. The former captures a dense LF, while the latter captures a sparse LF. This paper presents a generic compression scheme that efficiently compresses both densely and sparsely sampled LFs. A plenoptic image is converted into sub-aperture images, and each sub-aperture image is interpreted as a frame of a multiview sequence. In comparison, each view of the multi-camera system is treated as a frame of a multi-view sequence. The multi-view extension of high efficiency video coding (MVHEVC) is used to encode the pseudo multi-view sequence.This paper proposes an adaptive prediction and rate allocation scheme that efficiently compresses LF data irrespective of the acquisition technology used.

The acquisition of the spatial and angular information of a scene using light eld (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 efciently exploits the spatial and angular correlation using a multiview extension of high-efciency 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).

Light field (LF) acquisition devices capture spatial and angular information of a scene. In contrast with traditional cameras, the additional angular information enables novel post-processing applications, such as 3D scene reconstruction, the ability to refocus 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 were divided into two groups: key views and decimated views. The key views were compressed using the multi-view extension of high-efficiency video coding (MV-HEVC) scheme, and decimated views were predicted using the shearlet-transform-based prediction (STBP) scheme. Additionally, the residual information of predicted views was also encoded and sent along with the coded stream of key views. The proposed scheme was evaluated over a benchmark multi-camera based LF datasets, demonstrating that incorporating the residual information into the compression scheme increased the overall peak signal to noise ratio (PSNR) by 2 dB. The proposed compression scheme performed significantly better at low bit rates compared to anchor schemes, which have a better level of compression efficiency in high bit-rate scenarios. The sensitivity of the human vision system towards compression artifacts, specifically at low bit rates, favors the proposed compression scheme over anchor schemes. The proposed compression scheme performed significantly better at low bit rates compared to anchor schemes, which have a better level of compression efficiency in high bit-rate scenarios. The sensitivity of the human vision system towards compression artifacts, specifically at low bit rates, favors the proposed compression scheme over anchor schemes. The proposed compression scheme performed significantly better at low bit rates compared to anchor schemes, which have a better level of compression efficiency in high bit-rate scenarios. The sensitivity of the human vision system towards compression artifacts, specifically at low bit rates, favors the proposed compression scheme over anchor schemes.