DSP TMS320C6678 Based SHVC Encoder Implementation and its Optimization

The programmable processors newest technologies, as for example the multicore Digital Signal Processors (DSP), offer a promising solution for overcoming the complexity of the real time video encoding application. In this paper, the SHVC video encoder was effectively implemented just on a single core among the eight cores of TMS320C6678 DSP for a Common Intermediate Format (CIF)input video sequence resolution(352x288). Performance optimization of the SHVC encoder had reached up 41% compared to its reference software enabling a real-time implementation of the SHVC encoder for CIF input videos sequence resolution. The proposed SHVC implementation was carried out on different quantization parameters (QP). Several experimental tests had proved our performance achievement for real-time encoding on TMS320C6678.

Optimization of Video Cloud Gaming Using Fast HEVC Video Compression Technique

Cloud computing is a model of technology that offers access to system resources with advanced level of services ability. These resources are measured reliable, flexible and affordable for several kinds of applications and users. Gaming manufacturing is one filed that expands the profits of cloud computing as numerous new cloud gaming designs have been presented. Many advantages of cloud gaming have exaggerated the success of gaming based on the improvements on traditional online gaming. Though, cloud gaming grieves from several downsides such as the massive amount of needed video processing and the computational complexity required for that. This paper displays the original system drawbacks and develops a new and original algorithm to speed up the encoding process by reduces the computational complexity by exploiting the block type and location. Enhancements on the video codec led to 12.2% speeding up on the over-all encoding time with slight loss of users’ satisfactions. Keywords: Cloud gaming, Computational complexity, Motion estimation, HEVC, Video Encoding

A Novel Video Transmission Optimization Mechanism Based on Reinforcement Learning and Edge Computing

As we know, the video transmission traffic already constitutes 60% of Internet downlink traffic. The optimization of video transmission efficiency has become an important challenge in the network. This paper designs a video transmission optimization strategy that takes reinforcement learning and edge computing (TORE) to improve the video transmission efficiency and quality of experience. Specifically, first, we design the popularity prediction model for video requests based on the RL (reinforcement learning) and introduce the adaptive video encoding method for optimizing the efficiency of computing resource distribution. Second, we design a video caching strategy, which adopts EC (edge computing) to reduce the redundant video transmission. Last, simulations are conducted, and the experimental results fully demonstrate the improvement of video quality and response time.

JSCC-Cast: A Joint Source Channel Coding Video Encoding and Transmission System with Limited Digital Metadata

This work considers the design and practical implementation of JSCC-Cast, a comprehensive analog video encoding and transmission system requiring a reduced amount of digital metadata. Suitable applications for JSCC-Cast are multicast transmissions over time-varying channels and Internet of Things wireless connectivity of end devices having severe constraints on their computational capabilities. The proposed system exhibits a similar image quality compared to existing analog and hybrid encoding alternatives such as Softcast. Its design is based on the use of linear transforms that exploit the spatial and temporal redundancy and the analog encoding of the transformed coefficients with different protection levels depending on their relevance. JSCC-Cast is compared to Softcast, which is considered the benchmark for analog and hybrid video coding, and with an all-digital H.265-based encoder. The results show that, depending on the scenario and considering image quality metrics such as the structural similarity index measure, the peak signal-to-noise ratio, and the perceived quality of the video, JSCC-Cast exhibits a performance close to that of Softcast but with less metadata and not requiring a feedback channel in order to track channel variations. Moreover, in some circumstances, the JSCC-Cast obtains a perceived quality for the frames comparable to those displayed by the digital one.

Cloud Optimized Raster Encoding (CORE): A Web-Native Streamable Format for Large Environmental Time Series

The Environmental Data Portal EnviDat aims to fuse data publication repository functionalities with next-generation web-based environmental geospatial information systems (web-EGIS) and Earth Observation (EO) data cube functionalities. User requirements related to mapping and visualization represent a major challenge for current environmental data portals. The new Cloud Optimized Raster Encoding (CORE) format enables an efficient storage and management of gridded data by applying video encoding algorithms. Inspired by the cloud optimized GeoTIFF (COG) format, the design of CORE is based on the same principles that enable efficient workflows on the cloud, addressing web-EGIS visualization challenges for large environmental time series in geosciences. CORE is a web-native streamable format that can compactly contain raster imagery as a data hypercube. It enables simultaneous exchange, preservation, and fast visualization of time series raster data in environmental repositories. The CORE format specifications are open source and can be used by other platforms to manage and visualize large environmental time series.

Content Dependent Representation Selection Model for Systems Based on MPEG DASH

A standard called MPEG Dynamic Adaptive Streaming over HTTP (MPEG DASH) ensures the interoperability between different streaming services and the highest possible video quality in changing network conditions. The solutions described in the available literature that focus on video segmentation are mostly proprietary, use a high amount of computational power, lack the methodology, model notation, information needed for reproduction, or do not consider the spatial and temporal activity of video sequences. This paper presents a new model for selecting optimal parameters and number of representations for video encoding and segmentation, based on a measure of the spatial and temporal activity of the video content. The model was developed for the H.264 encoder, using Structural Similarity Index Measure (SSIM) objective metrics as well as Spatial Information (SI) and Temporal Information (TI) as measures of video spatial and temporal activity. The methodology that we used to develop the mathematical model is also presented in detail so that it can be applied to adapt the mathematical model to another type of an encoder or a set of encoding parameters. The efficiency of the segmentation made by the proposed model was tested using the Basic Adaptation algorithm (BAA) and Segment Aware Rate Adaptation (SARA) algorithm as well as two different network scenarios. In comparison to the segmentation available in the relevant literature, the segmentation based on the proposed model obtains better SSIM values in 92% of cases and subjective testing showed that it achieves better results in 83.3% of cases.