DyPS: Dynamic Processor Switching for Energy-Aware Video Decoding on Multi-core SoCs

In addition to General Purpose Processors (GPP), Multicore SoCs equipping modern mobile devices contain specialized Digital Signal Processor designed with the aim to provide better performance and low energy consumption properties. However, the experimental measurements we have achieved revealed that system overhead, in case of DSP video decoding, causes drastic performances drop and energy efficiency as compared to the GPP decoding. This paper describes DyPS, a new approach for energy-aware processor switching (GPP or DSP) according to the video quality . We show the pertinence of our solution in the context of adaptive video decoding and describe an implementation on an embedded Linux operating system with the help of the GStreamer framework. A simple case study showed that DyPS achieves 30% energy saving while sustaining the decoding performanc

Low computational complexity variable block size (VBS) partitioning for motion estimation using the Walsh Hadamard transform (WHT)

Variable Block Size (VBS) based motion estimation has been adapted in state of the art video coding, such as H.264/AVC, VC-1. However, a low complexity H.264/AVC encoder cannot take advantage of VBS due to its power consumption requirements. In this paper, we present a VBS partition algorithm based on a binary motion edge map without either initial motion estimation or Rate-Distortion (R-D) optimization for selecting modes. The proposed algorithm uses the Walsh Hadamard Transform (WHT) to create a binary edge map, which provides a computational complexity cost effectiveness compared to other light segmentation methods typically used to detect the required region

Complexity management of H.264/AVC video compression.

The H. 264/AVC video coding standard offers significantly improved compression efficiency and flexibility compared to previous standards. However, the high computational complexity of H. 264/AVC is a problem for codecs running on low-power hand held devices and general purpose computers. This thesis presents new techniques to reduce, control and manage the computational complexity of an H. 264/AVC codec. A new complexity reduction algorithm for H. 264/AVC is developed. This algorithm predicts "skipped" macroblocks prior to motion estimation by estimating a Lagrange ratedistortion cost function. Complexity savings are achieved by not processing the macroblocks that are predicted as "skipped". The Lagrange multiplier is adaptively modelled as a function of the quantisation parameter and video sequence statistics. Simulation results show that this algorithm achieves significant complexity savings with a negligible loss in rate-distortion performance. The complexity reduction algorithm is further developed to achieve complexity-scalable control of the encoding process. The Lagrangian cost estimation is extended to incorporate computational complexity. A target level of complexity is maintained by using a feedback algorithm to update the Lagrange multiplier associated with complexity. Results indicate that scalable complexity control of the encoding process can be achieved whilst maintaining near optimal complexity-rate-distortion performance. A complexity management framework is proposed for maximising the perceptual quality of coded video in a real-time processing-power constrained environment. A real-time frame-level control algorithm and a per-frame complexity control algorithm are combined in order to manage the encoding process such that a high frame rate is maintained without significantly losing frame quality. Subjective evaluations show that the managed complexity approach results in higher perceptual quality compared to a reference encoder that drops frames in computationally constrained situations. These novel algorithms are likely to be useful in implementing real-time H. 264/AVC standard encoders in computationally constrained environments such as low-power mobile devices and general purpose computers

Distributed Coding/Decoding Complexity in Video Sensor Networks

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Complexity adaptation in video encoders for power limited platforms

With the emergence of video services on power limited platforms, it is necessary to consider both performance-centric and constraint-centric signal processing techniques. Traditionally, video applications have a bandwidth or computational resources constraint or both. The recent H.264/AVC video compression standard offers significantly improved efficiency and flexibility compared to previous standards, which leads to less emphasis on bandwidth. However, its high computational complexity is a problem for codecs running on power limited plat- forms. Therefore, a technique that integrates both complexity and bandwidth issues in a single framework should be considered. In this thesis we investigate complexity adaptation of a video coder which focuses on managing computational complexity and provides significant complexity savings when applied to recent standards. It consists of three sub functions specially designed for reducing complexity and a framework for using these sub functions; Variable Block Size (VBS) partitioning, fast motion estimation, skip macroblock detection, and complexity adaptation framework. Firstly, the VBS partitioning algorithm based on the Walsh Hadamard Transform (WHT) is presented. The key idea is to segment regions of an image as edges or flat regions based on the fact that prediction errors are mainly affected by edges. Secondly, a fast motion estimation algorithm called Fast Walsh Boundary Search (FWBS) is presented on the VBS partitioned images. Its results outperform other commonly used fast algorithms. Thirdly, a skip macroblock detection algorithm is proposed for use prior to motion estimation by estimating the Discrete Cosine Transform (DCT) coefficients after quantisation. A new orthogonal transform called the S-transform is presented for predicting Integer DCT coefficients from Walsh Hadamard Transform coefficients. Complexity saving is achieved by deciding which macroblocks need to be processed and which can be skipped without processing. Simulation results show that the proposed algorithm achieves significant complexity savings with a negligible loss in rate-distortion performance. Finally, a complexity adaptation framework which combines all three techniques mentioned above is proposed for maximizing the perceptual quality of coded video on a complexity constrained platform