Complexity Rate Distortion Research Articles

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

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 first performs an analysis to determine the tradeoff between the decoding complexity, video quality, and bit rate with respect to a reference decoder implementation on a general purpose processor architecture. Thereafter, 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.

Hybrid Laplace Distribution-Based Low Complexity Rate-Distortion Optimized Quantization.

Rate distortion optimized quantization (RDOQ) is an efficient encoder optimization method that plays an important role in improving the rate-distortion (RD) performance of the high-efficiency video coding (HEVC) codecs. However, the superior performance of RDOQ is achieved at the expense of high computational complexity cost in two stages RD minimization, including the determination of optimal quantized level among available candidates for each transformed coefficient and the determination of best quantized coefficients for transform units with the minimum total cost, to softly optimize the quantized coefficients. To reduce the computational cost of the RDOQ algorithm in HEVC, we propose a low-complexity RDOQ scheme by modeling the statistics of the transform coefficients with hybrid Laplace distribution. In this manner, specifically designed block level rate and distortion models are established based on the coefficient distribution. Therefore, the optimal quantization levels can be directly determined by optimizing the RD performance of the whole block, while the complicated RD cost calculations can be eventually avoided. Extensive experimental results show that with about 0.3%-0.4% RD performance degradation, the proposed low-complexity RDOQ algorithm is able to reduce around 70% quantization time with up to 17% total encoding time reduction compared with the original RDOQ implementation in HEVC on average.

Optimal model-based complexity control for H.264 video encoding

An H.264 video encoder adopts multiple encoding tools to achieve high coding efficiency at the expense of high computational complexity. The allowable computational complexity for real-time video encoding, however, is generally limited in a wireless handset. This research proposes a complexity control mechanism that is composed of two algorithms to minimise the distortion of each encoded video frame under the computational complexity constraint and the rate constraint. The first proposed algorithm performs optimal complexity allocation among encoding tools based on a new complexity–rate–distortion (C–R–D) model. This model precisely describes how each encoding tool influences the C–R–D performance of the encoder with concise formulas. Accordingly, the algorithm obtains the optimal complexity of each encoding tool by a closed-form solution with small complexity overhead. Based on a new C–D model of motion estimation, this work proposes the second algorithm that performs optimal complexity allocation among macro-blocks to further allocate suitable complexity to each macro-block. Experiments performed on a software-optimised source code show that these two algorithms yield superior performance to the existing algorithms.

Backward channel aware Wyner–Ziv video coding: A study of complexity, rate, and distortion tradeoff

Wyner–Ziv video coding is a coding method that exploits source statistics at the decoder. Many Wyner–Ziv video coding schemes encode a video sequence into two types of frames, key frames and Wyner–Ziv frames, and rely on the frequent use of INTRA frames as key frames such that the decoder can derive sufficiently accurate side information. While the use of INTRA frame as key frames keeps the encoding complexity low, it also leads to much lower coding efficiency compared to state-of-the-art predictive video coding. In this paper we present a Wyner–Ziv video coding scheme that uses backward channel aware motion estimation to encode the key frames, where motion estimation is performed at the decoder and motion information is sent back to the encoder. Two motion vector selection methods are proposed and we present a model to study the complexity–rate–distortion tradeoff in such coding schemes.

Dynamic computational complexity and bit allocation for optimizing H.264/AVC video compression

In this work, we present a novel approach for optimizing H.264/AVC video compression by dynamically allocating computational complexity (such as a number of CPU clocks) and bits for encoding each coding element (basic unit) within a video sequence, according to its predicted MAD (mean absolute difference). Our approach is based on a computational complexity–rate–distortion (C–R–D) analysis, which adds a complexity dimension to the conventional rate–distortion (R–D) analysis. Both theoretically and experimentally, we prove that by implementing the proposed approach for the dynamic allocation better results are achieved. We also prove that the optimal computational complexity allocation along with optimal bit allocation is better than the constant computational complexity allocation along with optimal bit allocation. In addition, we present a method and system for implementing the proposed approach, and for controlling computational complexity and bit allocation in real-time and off-line video coding. We divide each frame into one or more basic units, wherein each basic unit consists of at least one macroblock (MB), whose contents are related to a number of coding modes. We determine how much computational complexity and bits should be allocated for encoding each basic unit, and then allocate a corresponding group of coding modes and a quantization step-size, according to the estimated distortion (calculated by a linear regression model) of each basic unit and according to the remaining computational complexity and bits for encoding remaining basic units. For allocating the corresponding group of coding modes and the quantization step-size, we develop computational complexity–complexity step–rate (C–I–R) and rate–quantization step-size–computational complexity (R–Q–C) models.