A feature based complexity model for decoder complexity optimized HEVC video encoding

Mallikarachchi, Thanuja; Talagala, Dumidu S.; Arachchi, Hemantha Kodikara; Fernando, Anil

doi:10.1109/icce.2017.7889357

Cited by 11 publications

(12 citation statements)

References 13 publications

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“…In the literature, many models were proposed for estimating the decoding energy and the decoding power [10], [11], [22], [23], [28], [29]. As most modern smartphones provide hardware video decoder modules that are more power-efficient than software video decoders [15], we only consider a hardware decoder model proposed in [25].…”

Section: B Video Processing Powermentioning

confidence: 99%

Power Modeling for Video Streaming Applications on Mobile Devices

et al. 2020

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In this paper, we derive an accurate power model for video streaming which we condense to the essential components contributing the most to the overall power consumption. As a use case, we choose mobile devices on the receiver side performing video streaming in broadcasting or end-to-end scenarios. In modeling, we consider the complete video streaming toolchain, which mainly consists of data acquisition, video processing, display, and audio handling. We compose an overall power model with the help of models from the literature and propose a dedicated feature selection approach to reveal the most important factors related to power consumption. The resulting models achieve mean estimation errors below 7.61%. Results from feature selection indicate that the display brightness, the bitrate, and the frame rate have the highest impact on the power consumption.

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Section: B Video Processing Powermentioning

confidence: 99%

Power Modeling for Video Streaming Applications on Mobile Devices

et al. 2020

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“…As such, a content-adaptive decoding-complexity-rate-distortion model is necessary, where the decoding-complexity can be determined for various decoding operations based on the coding modes and features selected by the encoder. To achieve this, the decoding-complexity estimation models developed in [24,[43][44][45] for both inter-predicted and intra-predicted coding units (CU) are used as a basis for this work, which will equip the encoder to compute the relative complexity of each decoding operation. This section describes the approach used to analyze the behavior of these three parameters and a content-dependent model that can be generated for the decoding-complexity-rate-distortion space.…”

Section: The Decoding-complexity Rate and Distortion Relationshipmentioning

confidence: 99%

“…where λ ≥ 0 is the empirically determined Lagrangian multiplier, p is a coding structure in the set of combinations P, and D(p) and R(p) represent the distortion (squared error per pixel) and bit rate (bits per pixel), respectively. Each p in (1) results in different decoding-complexities at the decoder [24,[44][45][46] that remain unknown to the encoder. In order to assess the impact of each p on the decoding-complexity, we first redefine the optimization function in (1) as…”

Section: The Decoding-complexity Rate and Distortion Spacementioning

confidence: 99%

“…The proposed encoding algorithm is implemented in the HM 16.0 reference encoder. The decoding-complexity estimation models presented in [24,[43][44][45], the Lagrangian cost function that determines the coding modes and the proposed decoding-complexity, and rate-controlling algorithm, are integrated into the HEVC encoding tool chain. The resultant bit streams are decoded using the openHEVC [54] software decoder.…”

Section: Simulation Environmentmentioning

confidence: 99%

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

et al. 2020

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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.

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“…To this end, a decoding complexity profiling for individual decoding operations relating to the HEVC coding modes and features has been carried out in our previous works [27][28] using open source instruction level profiling tools [34]. In this context, the computational complexities in terms of CPU cycles identified in [27] [28] are embedded within HM16.0 implementation to make the encoder aware of the relative complexities of the decoding operations (The CU level decoding complexity estimation models in [27] and [28] for intra-and inter-prediction, respectively, have been verified to predict the decoding complexity within the encoder with only < 5% prediction error). The encoder now possesses the resulting bit rate, distortion and decoding complexity for a particular coding mode and QP of a given content which can then be used to form the decoding complexity, rate and distortion analysis, as described next.…”

Section: Decoding Complexity -Rate -Distortion Analysis For Encomentioning

confidence: 99%

Decoding-Complexity-Aware HEVC Encoding Using a Complexity–Rate–Distortion Model

Mallikarachchi

Talagala

Arachchi

et al. 2018

IEEE Trans. Consumer Electron.

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Abstract-The energy consumption of Consumer Electronic (CE) devices during media playback is inexorably linked to the computational complexity of decoding compressed video. Reducing a CE device's the energy consumption is therefore becoming ever more challenging with the increasing video resolutions and the complexity of the video coding algorithms. To this end, this paper proposes a framework that alters the video bit stream to reduce the decoding complexity and simultaneously limits the impact on the coding efficiency. In this context, this paper (i) first performs an analysis to determine the trade-off between the decoding complexity, video quality and bit rate with respect to a reference decoder implementation on a General Purpose Processor (GPP) architecture. Thereafter, (ii) a novel generic decoding complexity-aware video coding algorithm is proposed to generate decoding complexity-rate-distortion optimized High Efficiency Video Coding (HEVC) bit streams. The experimental results reveal that the bit streams generated by the proposed algorithm achieve 29.43% and 13.22% decoding complexity reductions for a similar video quality with minimal coding efficiency impact compared to the state-of-the-art approaches when applied to the HM16.0 and openHEVC decoder implementations, respectively. In addition, analysis of the energy consumption behavior for the same scenarios reveal up to 20% energy consumption reductions while achieving a similar video quality to that of HM 16.0 encoded HEVC bit streams.

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A feature based complexity model for decoder complexity optimized HEVC video encoding

Cited by 11 publications

References 13 publications

Power Modeling for Video Streaming Applications on Mobile Devices

Power Modeling for Video Streaming Applications on Mobile Devices

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

Decoding-Complexity-Aware HEVC Encoding Using a Complexity–Rate–Distortion Model

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