Mean Absolute Difference Prediction Research Articles

A Video Bit Rate Control Algorithm Based on Weighted Estimate of Image Brightness Difference

Rate control is the core issue to realize video encoder, and it is also one of the key factors determining the quality of video code system. Given the MAD (Mean Absolute Difference) prediction and the drawback of bit rate control in the BU (Basic Unit) layer, improvements are put forward on the basis of analyzing and researching the G012 rate control algorithms in this paper. In this paper, we proposed a new video bit rate control algorithm to overcome the drawback of bit rate control in the BU (Basic Unit) layer. Combined with the image brightness gradient value to estimate in the MAD prediction, the allocation encoding bits method based on the PSNR (Peak Signal to Noise Ratio) is proposed in the rate control of BU layer. The experiment results show that compared with JM algorithms in H.264 standard reference software, the improved algorithm is enhanced in the PSNR.

A rate-control algorithm using inter-layer information for H.264/SVC for low-delay applications

This paper proposes a novel frame-level rate-control scheme for H.264/SVC for low-delay applications. The trend of the Mean Absolute Difference (MAD) (after motion compensation) of the reference layer is used as the inter-layer information for Inter-layer MAD prediction, which is combined with Intra-layer MAD prediction to predict the MAD of the enhancement layers (EL) more accurately. By considering the influence from both the texture and nontexture information, the varying picture complexity and feedback information from the actual encoding results are combined to achieve an accurate bit-allocation. A coarse-to-fine initial quantization parameter (QP) selection method is proposed to refine the initial QP of the EL according to the channel condition and the video sequence characteristics. Experimental results demonstrate that the proposed scheme can obtain high and smooth PSNR, and the output bit-rate is close to the target bit-rate. Also, the proposed scheme can avoid serious buffer fullness fluctuation and reduce skipped frames in the video coding.

A Context-Adaptive Prediction Scheme for Parameter Estimation in H.264/AVC Macroblock Layer Rate Control

In this paper, we present a novel context-adaptive model parameter prediction scheme for improving the estimation accuracy of the mean absolute difference (MAD) of texture and other model parameters in the linear rate quantization (R-Q) model-based H.264/AVC macroblock layer rate control for low bit rate real-time applications. The context-adaptive prediction scheme simultaneously exploits both spatial correlations and temporal correlations among the neighboring macroblocks within a so-called context of a macroblock. The location and shape of the context as well as the number of neighboring macroblocks in the context are adaptively computed according to local video signal characteristics using a Manhattan distance metric and an improved 2-D sliding window method. The proposed context-adaptive model parameter prediction scheme effectively suppresses the detrimental oscillations of estimated model parameters. Extensive experiments show that compared to the recent H.264/AVC reference software, macroblock layer rate control algorithm using our proposed context-adaptive prediction scheme significantly improves the MAD and model parameter prediction accuracy and bit achievement accuracy, and hence obtains much better rate distortion performance.

A Novel Rate Control Scheme for Low Delay Video Communication of H.264/AVC Standard

This paper presents a novel rate control scheme for low delay video communication of H.264/AVC standard. A switched mean-absolute-difference (MAD) prediction scheme is introduced to enhance the traditional temporal MAD prediction model, which is not suitable for predicting abrupt MAD fluctuations. Our new model could reduce the MAD prediction error by up to 69%. Furthermore, an accurate linear rate-quantization (R-Q) model is also formulated to describe the relationship between the total amount of bits for both texture and nontexture information and the quantization parameter (QP), so that the negative effect caused by the inaccurate estimation of nontexture bits is removed. By exploring the relationship between peak signal-to-noise ratio and QP value, the proposed linear R-Q model could further optimize QP calculation at the macroblock level. When compared with the rate control scheme JVT-G012 which is adopted by the latest JVT H.264/AVC reference model JM9.8, the proposed rate control algorithm could reduce the mismatch between actual bits and target ones by up to 75%. To meet the low delay requirement, the buffer is better controlled to prevent overflowing and underflowing. The average luminance PSNR of reconstructed video is increased by up to 1.13 dB at low bit rates, and the subjective video quality is also improved