Ultra High Definition Television (UHDTV) offers a better immersive audiovisual experience than HDTV by improving the aesthetic sense of the content [1]. How-ever, it may lead to an increase of both encoding time complexity and compression artifacts at lower bitrates. To address this challenge, a low-latency pre-processing algorithm named COntent-aware frame Dropping Algorithm (CODA) is proposed to predict the optimized framerate per video segment in streaming scenarios. The optimized framerate$(\hat{f})$for every video segment at each target bitrate is modelled as an exponential decay (increasing) function whose decay rate is directly proportional to the temporal characteristics$(h)$[2] [3] of the video and the target bitrate$(b)$, and inversely proportional to the spatial characteristics$(E)$of the video. The encoding is carried out with the predicted framerate, saving encoding time and improving visual quality at lower bitrates. At the decoder side, the video is upscaled in the temporal domain to the original framerate$(f_{max})$for display.

Light field imaging enables some post-processing capabilities like refocusing, changing view perspective, and depth estimation. As light field images are represented by multiple views, they contain a huge amount of data that makes compression inevitable. Although there are some proposals to efficiently compress light field images, their main focus is on encoding efficiency. However, some important functionalities such as viewpoint and quality scalabil- ities, random access, and uniform quality distribution have not been addressed adequately. In this paper, an efficient light field image compression method based on a deep neural network is proposed, which classifies multiple views into various layers. In each layer, the target view is synthesized from the available views of previously encoded/decoded layers using a deep neural network. This synthesized view is then used as a virtual reference for the target view inter-coding. In this way, random access to an arbitrary view is provided. Moreover, uniform quality distribution among multiple views is addressed. In higher bitrates where random access to an arbitrary view is more crucial, the required bitrate to access the requested view is minimized.

Adaptive HTTP streaming provides multiple representations of the same content at different bit-rates and resolutions and allows the client to request segments from the available representations in a dynamic, adaptive way depending on its context. The growing number of representations in adaptive HTTP streaming makes encoding of one video segment at different representations a challenging task in terms of encoding time-complexity. In this paper, information of both highest and lowest quality representations are used to limit Rate Distortion Optimization (RDO) process for each Coding Unit Tree (CTU) in High Efficiency Video Coding. Our proposed method first encodes the highest quality representation and consequently uses its information to encode the lowest quality representation. Thereafter, information from both the highest and the lowest quality representations are used to predict features of intermediate quality representations. In particular, the block structure and the selected reference frame of both highest and lowest quality representations are used to predict and shorten the RDO process of each CTU for intermediate quality representations. Our proposed method introduces a delay of two CTUs if parallel encoding is used. Experimental results show significant reduction in time-complexity over the reference software (38%) and the state-of-the-art (10%) while quality degradation is negligible.

Pseudo-sequence based light field compression methods are a highly efficient solution to compress light field images. They use state-of-the-art video encoders like HEVC to encode the image views. HEVC exploits Coding Tree Unit (CTU) structure which is flexible and highly efficient but it is computationally demanding. Each CTU is examined in various depths, prediction and transformation modes to find an optimal coding structure. Efficiently predicting depth of the coding units can reduce complexity significantly. In this paper, a new depth decision method is introduced which exploits the minimum and maximum of previously encoded co-located coding units in spatially closer reference images. Minimum and maximum depths of these co-located CTUs are computed for each coding unit and are used to limit the depth of the current coding unit. Experimental results show up to 55% and 85% encoding time reduction with serial and parallel processing respectively, at negligible degradations.

In light field compression, besides coding efficiency, providing random access to individual views is also a very significant factor. Highly efficient compression methods usually lack random access. Similarly, random access methods usually reduce the compression efficiency. To address this trade-off, a light field image encoding method is proposed in this paper which favors random access. In the proposed scheme 15×15 view images are divided into 25 independent 3×3 view images which are called Macro View Image (MVI). To encode MVIs, the central view image is used to compress its immediate neighboring view images using a hierarchical reference structure. To encode the central view of each MVI, the most central view image, along with the center of at most three MVIs, are used as the reference images for the disparity estimation. In addition, the proposed method enables the use of parallel computation to improve encoding/decoding time complexity. To reduce memory footprint in case a Region of Interest (ROI) is required, HEVC tile partitioning is used.