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From the beginnings of ITU-T H.261 to H.265 (HEVC), each new video coding standard has aimed at halving the bitrate at the same perceptual quality by redundancy and irrelevancy reduction. Each improvement has been explained by comparably small changes in the video coding toolset. This contribution aims at starting the Quality of Experience (QoE) analysis of the accumulated improvements over the last thirty years. Based on an overview of the changes in the coding tools, we analyze the changes in the quantized residual information. Visual comparison and statistical measures are performed and some interpretations are provided towards explaining how irrelevancy reduction may have led to such a huge reduction in bitrate. The interpretation of the results in terms of QoE paves the way towards an understanding of the coding tools in terms of visual quality. It may help in understanding how the irrelevancy reduction has been improved over the decades. Understanding how the differences of the residuals relate to known or yet unknown properties of the human visual system, may enable a closer collaboration between perception research and video compression research.

The coding tools used in image and video encoders aim at high perceptual quality for low bitrates. Analyzing the results of the encoders in terms of quantization parameter, image partitioning, prediction modes or residuals may provide important insight into the link between those tools and the human perception. As a first step, this contribution analyzes the possibility to transcode reference images of three well-known image databases, i.e. IRCCyN/IVC, LIVE and TID2013, from their original, older formats to HEVC; thus creating a homogeneous database of 327 HEVC encoded images accompanied with bitstream parameters and values obtained from objective and subjective assessments. Secondly, it analyzes some of the HEVC intra coding parameters regarding their influence on the image quality by using machine learning, namely Support Vector Machine - Regression.

This paper deals with the influence of chroma subsampling on perceived video quality measured by subjective metrics. The evaluation was done for two most used video codecs H.264/AVC and H.265/HEVC. Eight types of video sequences with Full HD and Ultra HD resolutions depending on content were tested. The experimental results showed that observers did not see the difference between unsubsampled and subsampled sequences, so using subsampled videos is preferable even 50 % of the amount of data can be saved. Also, the minimum bitrates to achieve the good and fair quality by each codec and resolution were determined.

In this paper, the performance of the proposed Convolutional Neural Network (CNN) with three well-known image recognition methods such as Principal Component Analysis (PCA), Local Binary Patterns Histograms (LBPH) and K–Nearest Neighbour (KNN) is tested. In our experiments, the overall recognition accuracy of the PCA, LBPH, KNN and proposed CNN is demonstrated. All the experiments were implemented on the ORL database and the obtained experimental results were shown and evaluated. This face database consists of 400 different subjects (40 classes/ 10 images for each class). The experimental result shows that the LBPH provide better results than PCA and KNN. These experimental results on the ORL database demonstrated the effectiveness of the proposed method for face recognition. For proposed CNN we have obtained a best recognition accuracy of 98.3 %. The proposed method based on CNN outperforms the state of the art methods.

In recent years, an interest in multimedia services has become a global trend and this trend is still rising. The video quality is a very significant part from the bundle of multimedia services, which leads to a requirement for quality assessment in the video domain. Video quality of a streamed video across IP networks is generally influenced by two factors “transmission link imperfection and efficiency of compression standards. This paper deals with subjective video quality assessment and the impact of the compression standards H.264, H.265 and VP9 on perceived video quality of these compression standards. The evaluation is done for four full HD sequences, the difference of scenes is in the content“ distinction is based on Spatial (SI) and Temporal (TI) Index of test sequences. Finally, experimental results follow up to 30% bitrate reducing of H.265 and VP9 compared with the reference H.264.