The evolving landscape of the delivery of multimedia content requires a deep understanding of how the design of the bitrate ladder (for HTTP adaptive streaming) influences cost and quality. This paper explores the use of Just Noticeable Differences (JND) to select bitrate-resolution pairs for constructing a bitrate ladder with respect to the proportion of satisfied user ratio (SUR). To expand the investigation to various codecs, first, a method is explained that transfers the JND points obtained through subjective testing from one codec (e.g., , AVC) to other codecs (e.g., , HEVC, VVC). This approach helps avoid the additional costs associated with conducting subjective tests to obtain JND points for a wide range of different codecs. To achieve this objective, we investigate the codec-agnostic nature of various video quality metrics, followed by the transfer of JND between two codecs, taking into account the most suitable codec-agnostic video quality metric. Secondly, we delve into the analysis of the bitrate cost of a given bitrate ladder from a JND perspective, i.e., , as a function of the SUR. Among others, our experimental results demonstrate that increasing SUR leads to an exponential increase in bitrate. For example, to raise the SUR from 75% to 90%, it is necessary to double the video bitrate.

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.