A Decoding-Complexity and Rate-Controlled Video-Coding Algorithm for HEVC

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.

Temporal Context Mining for Learned Video Compression

“…The complexity of the decoder affects the compression performance [14], [61]. To explore the influence of the complexity of the video reconstruction network in our proposed framework on the performance of video compression, we perform another experiment by replacing the video reconstruction network which is made up of two cascaded U-Nets with one U-Net, two residual blocks, and one residual block.…”

Multimedia Quality of Experience (QoE): Current Status and Future Direction

“…The third paper [8], written by Thanuja Mallikarachchi, Dumidu Talagala, Hemantha Kodikara Arachchi, Chaminda Hewage and Anil Fernando, identifies the importance of addressing the control of decoding complexity and energy use when decoding video to improve user QoE. As a solution, this study proposes an encoding framework that is capable of generating video bit streams with arbitrary bit rate and decoding complexity levels using a decoding complexity rate distortion model.…”

Video Decoding Energy Reduction Using Temporal-Domain Filtering

“…Concerning the latter, certain studies revealed that the decoder is a major reason for a high energy consumption in portable devices such as smartphones or tablet PCs [7,12], especially if software decoding is used [16,19]. As a consequence, various studies focused on reducing the energy consumption of video decoders [8,11,13,26] while keeping the visual quality of the video. Also for the encoder, various studies focus on the energy consumption or the complexity [28,33,39] in order to obtain a high compression while using little energy resources.…”