A no-reference video quality assessment (VQA) method is presented for videos distorted by H.264/AVC and MPEG-2. The assessment is performed without access to the bit-stream. Instead we analyze and estimate coefficients based on decoded pixels. The approach involves distinguishing between the two types of videos, estimating the level of quantization used in the I-frames, and exploiting this information to assess the video quality. In order to do this for H.264/AVC, the distribution of the

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... fficients after intra-prediction and deblocking are modeled. To obtain VQA features for H.264/AVC, we propose a novel estimation method of the quantization in H.264/AVC videos without bitstream access, which can also be used for Peak Signalto-Noise Ratio (PSNR) estimation. The results from the MPEG-2 and H.264/AVC analysis are mapped to a perceptual measure of video quality by Support Vector Regression (SVR). For validation purposes, the proposed method was tested on two databases. In both cases good performance compared with state of the art full, reduced, and no-reference VQA algorithms was achieved.