Abstract
Video playback on mobile consumer electronic (CE) devices is plagued by fluctuations in the network bandwidth and by limitations in processing and energy availability at the individual devices. Seen as a potential solution, the state-of-the-art adaptive streaming mechanisms address the first aspect, yet the efficient control of the decoding-complexity and the energy use when decoding the video remain unaddressed. The quality of experience (QoE) of the end-users’ experiences, however, depends on the capability to adapt the bit streams to both these constraints (i.e., network bandwidth and device’s energy availability). As a solution, this paper proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rates and decoding-complexity levels using a decoding-complexity–rate–distortion model. The proposed algorithm allocates rate and decoding-complexity levels across frames and coding tree units (CTUs) and adaptively derives the CTU-level coding parameters to achieve their imposed targets with minimal distortion. The experimental results reveal that the proposed algorithm can achieve the target bit rate and the decoding-complexity with 0.4% and 1.78% average errors, respectively, for multiple bit rate and decoding-complexity levels. The proposed algorithm also demonstrates a stable frame-wise rate and decoding-complexity control capability when achieving a decoding-complexity reduction of 10.11 (%/dB). The resultant decoding-complexity reduction translates into an overall energy-consumption reduction of up to 10.52 (%/dB) for a 1 dB peak signal-to-noise ratio (PSNR) quality loss compared to the HM 16.0 encoded bit streams.
Highlights
Mobile consumption of video content, advancements in consumer electronics, and the popularity of video on-demand services, have immensely contributed towards the dramatic increase in video data traffic in the Internet
It currently lacks the necessary awareness to alter the video content based on its decoding-complexity to reduce the energy demand on the end user’s device. (The term decoding-complexity in this manuscript refers to the number of CPU instructions consumed by the processor to decode an encoded bit stream.) This will become more pronounced with time, especially due to the complexity of modern standards such as High Efficiency Video Coding (HEVC) and the upcoming Versatile Video Coding (VVC), and the increasing demand for high definition (HD) content on hand-held devices [3,4]
The proposed algorithm’s performance is compared with three state-of-the-art approaches: the power-aware encoding algorithm proposed by He et al [20]; the rate, distortion, and decoder energy optimized encoding algorithm proposed by Herglotz et al [46]; and the tunable HEVC decoder proposed by Nogues et al [34]
Summary
Mobile consumption of video content, advancements in consumer electronics, and the popularity of video on-demand services, have immensely contributed towards the dramatic increase in video data traffic (expected to exceed 75% of the overall data traffic [1]) in the Internet. Existing content adaptation algorithms manipulate the bit rate, quantization parameter (QP), spatial resolution, etc., in an attempt to reduce the decoding-complexity and the device’s energy consumption [19,20,21,22,23], which eventually results in poor visual quality and minimal decoding-complexity reductions In contrast to these approaches, the authors’ previous work in [24] proposes a decoding-complexity–rate–distortion model within the encoder to determine the coding modes that minimize the joint rate, decoding-complexity, and distortion cost for a given QP.
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