A Matlab-Based Tool for Video Quality Evaluation without Reference

This paper deals with the design of a Matlab based tool for measuring video quality with no use of a reference sequence. The main goals are described and the tool and its features are shown. The paper begins with a description of the existing pixel-based no-reference quality metrics. Then, a novel algorithm for simple PSNR estimation of H.264/AVC coded videos is presented as an alternative. The algorithm was designed and tested using publicly available video database of H.264/AVC coded videos. Cross-validation was used to confirm the consistency of results

Estimating PSNR in High Definition H.264/AVC Video Sequences Using Artificial Neural Networks

The paper presents a video quality metric designed for the H.264/AVC codec. The metric operates directly on the encoded H.264/AVC bit stream, parses the encoding parameters and processes them using an artificial neural network. The network is designed to estimate peak signal-to-noise ratios of the video sequence frames, thus enabling computation of full reference objective quality metric values without having the undistorted video material prior to encoding for comparison. We present the metric framework and test its performance for LDTV (low definition television) as well as HDTV (high definition television) video material

Perceptual video quality assessment in H.264 video coding standard using objective modeling

Since usage of digital video is wide spread nowadays, quality considerations have become essential, and industry demand for video quality measurement is rising. This proposal provides a method of perceptual quality assessment in H.264 standard encoder using objective modeling. For this purpose, quality impairments are calculated and a model is developed to compute the perceptual video quality metric based on no reference method. Because of the shuttle difference between the original video and the encoded video the quality of the encoded picture gets degraded, this quality difference is introduced by the encoding process like Intra and Inter prediction. The proposed model takes into account of the artifacts introduced by these spatial and temporal activities in the hybrid block based coding methods and an objective modeling of these artifacts into subjective quality estimation is proposed. The proposed model calculates the objective quality metric using subjective impairments; blockiness, blur and jerkiness compared to the existing bitrate only calculation defined in the ITU G 1070 model. The accuracy of the proposed perceptual video quality metrics is compared against popular full reference objective methods as defined by VQEG

Reduced-Reference Video Quality Metric Using Spatial Information in Salient Regions

In multimedia transmission, it is important to rely on an objective quality metric which accurately represents the subjective quality of processed images and video sequences. Maintaining acceptable Quality of Experience in video transmission requires the ability to measure the quality of the video seen at the receiver end. Reduced-reference metrics make use of side-information that is transmitted to the receiver for estimating the quality of the received sequence with low complexity. This attribute enables real-time assessment and visual degradation detection caused by transmission and compression errors. A novel reduced-reference video quality known as the Spatial Information in Salient Regions Reduced Reference Metric is proposed. The approach proposed makes use of spatial activity to estimate the received sequence distortion after concealment. The statistical elements analysed in this work are based on extracted edges and their luminance distributions. Results highlight that the proposed edge dissimilarity measure has a good correlation with DMOS scores from the LIVE Video Database

Digital Video Image Quality

Optical physic

Reduced-Reference video quality metric using spatial information in Salient Regions

In multimedia transmission, it is important to rely on an objective quality metric which accurately represents the subjective quality of processed images and video sequences. Maintaining acceptable Quality of Experience in video transmission requires the ability to measure the quality of the video seen at the receiver end. Reduced-reference metrics make use of side-information that is transmitted to the receiver for estimating the quality of the received sequence with low complexity. This attribute enables real-time assessment and visual degradation detection caused by transmission and compression errors. A novel reduced-reference video quality known as the Spatial Information in Salient Regions Reduced Reference Metric is proposed. The approach proposed makes use of spatial activity to estimate the received sequence distortion after concealment. The statistical elements analysed in this work are based on extracted edges and their luminance distributions. Results highlight that the proposed edge dissimilarity measure has a good correlation with DMOS scores from the LIVE Video Database

Implementing a Video Quality Metric in the H.264/AVC Decoder

V tejto diplomovej práci je predstavený a aplikovaný algoritmus pre hodnotenie kvality videosekvencií zakódovaných pomocou štandardu H.264. Ako merítko kvality obrazu je použitá objektívna metrika špičkový pomer signálu k šumu (PSNR). Zatiaľ čo výpočet PSNR zvyčajne vyžaduje referenčný signál, a ten porovnáva so skresleným, tento algoritmus dokáže vyčísliť PSNR na základe kódovaných transformačných koeficientov. A teda nie je potrebný žiadny referenčný signál.In this diploma thesis an algorithm for the evaluation of picture quality of H.264-coded video sequences is introduced and applied. As a measure of picture quality objective metric the peak signal to noise ratio (PSNR) is used. While the computation of the PSNR usually requires a reference signal and compares it to the distorted video sequence, this algorithm is able to evaluate PSNR following the coded transform coefficients. Thus, no reference signal is needed.