【摘要】：碼率控制是視頻編碼器中的關(guān)鍵模塊,其算法直接決定編碼器率失真性能.視頻編碼幀間預(yù)測導(dǎo)致的編碼失真會在時域產(chǎn)生傳遞效應(yīng),考慮該傳遞效應(yīng)是優(yōu)化碼率控制算法性能的關(guān)鍵.宏塊樹碼率控制是一種典型的時域量化控制算法,核心是根據(jù)編碼單元失真?zhèn)鬟f量(相對傳遞代價ρ)自適應(yīng)地調(diào)整量化參數(shù)(偏移量δ),合適的δ-ρ映射關(guān)系是宏塊樹量化控制算法的核心.宏塊樹算法采用基于經(jīng)驗的δ-ρ模型,對不同視頻序列的普適性有待改進,模型準確度和精度也需進一步優(yōu)化.針對上述問題,將競爭決策方法用于探索最優(yōu)δ-ρ映射關(guān)系,提出了一種率失真性能優(yōu)化的失真時域傳遞自適應(yīng)量化δ-ρ模型,以改進時域自適應(yīng)量化算法.實驗結(jié)果表明,信噪比BD-PSNR較原模型提升了0.14dB以上,SSIM性能提升了0.29dB.算法能更好地控制碼率時域分配,降低失真時域傳遞惡化.
[Abstract]:Rate control is a key module in video encoder, and its algorithm directly determines the rate distortion performance of encoder. The coding distortion caused by inter-frame prediction of video coding will produce transfer effect in time domain, which is the key to optimize the performance of the rate control algorithm. Macroblock rate control is a typical time-domain quantization control algorithm, which adaptively adjusts quantization parameters (offset 未) according to the distortion transfer amount (relative transfer cost 蟻) of the coding unit. The appropriate 未-蟻 mapping relation is the core of the macro block tree quantization control algorithm. The 未-蟻 model based on experience is adopted in macroblock tree algorithm. The universality of different video sequences needs to be improved, and the accuracy and accuracy of the model need to be further optimized. To solve the above problems, the competitive decision method is applied to explore the optimal 未-蟻 mapping relationship, and a time domain transfer adaptive quantization 未-蟻 model is proposed to improve the time domain adaptive quantization algorithm. The experimental results show that the signal-to-noise ratio (SNR) of BD-PSNR is higher than that of the original model, and the performance of SSIM is improved by 0.29 dB. The algorithm can better control rate time domain allocation and reduce distortion time domain transmission deterioration.
【作者單位】： 中國計量大學(xué)信息工程學(xué)院;
【基金】：國家自然科學(xué)基金資助項目(61572449) 浙江省自然科學(xué)基金資助項目(LY15F020022,LY13H180011)
【分類號】：TN919.81

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本文編號：2308154