This article proposes a pre-equalization method to remove inter-numerology interference (INI) that occurs in multi-numerology OFDM frame structures of fifth-generation New Radio (5G-NR) and beyond, on the transmitter side. In the literature, guard bands, filters, and interference cancellation methods are used to reduce the INI. In this work, we mathematically model how the INI is generated and show how it can be removed completely for multi-numerology systems by deploying a pre-equalization matrix on the transmitter side. With this pre-equalization method, the need for guard bands and filters is eliminated and spectral efficiency is improved.

Full-reference (FR) video quality estimators (QEs) resize either the distorted input video or the reference video to compute the quality when these videos have different spatial resolutions. This resizing operation causes several limitations. Multiscale Image Quality Estimator (MIQE) overcomes those limitations for images, but it does not consider the temporal characteristics of video. In this paper, we develop an FR video QE that integrates MIQE with the motion information to estimate the quality of the distorted video without resampling the reference or the test videos. We also perform subjective tests to compare the proposed algorithm with the existing QEs. In these tests, the reference and the input videos are displayed at their native resolutions. The test results show that the proposed algorithm outperforms other QEs when the reference video and the input video have different spatial resolutions. We have also evaluated the performance of the approach using the Scalable Video Database.

Full reference video quality estimators (QEs) either resize the input video or the reference video to compute the quality when these videos have different spatial resolutions. This resizing operation causes several limitations. Multiscale Image Quality Estimator (MIQE) [1] overcomes those limitations for images but it does not consider the temporal characteristics of video. In this work, we develop a video quality estimator that integrates MIQE with the motion information to estimate the quality. We also perform subjective tests to compare the proposed algorithm with the existing QEs. Test results show that the proposed algorithm outperforms other QEs.

Multimedia communication is becoming pervasive because of the progress in wireless communications and multimedia coding. Estimating the quality of the visual content accurately is crucial in providing satisfactory service. State of the art visual quality assessment approaches are effective when the input image and reference image have the same resolution. However, finding the quality of an image that has spatial resolution different than that of the reference image is still a challenging problem. To solve this problem, we develop a quality estimator (QE), which computes the quality of the input image without resampling the reference or the input images. In this paper, we begin by identifying the potential weaknesses of previous approaches used to estimate the quality of experience. Next, we design a QE to estimate the quality of a distorted image with a lower resolution compared with the reference image. We also propose a subjective test environment to explore the success of the proposed algorithm in comparison with other QEs. When the input and test images have different resolutions, the subjective tests demonstrate that in most cases the proposed method works better than other approaches. In addition, the proposed algorithm also performs well when the reference image and the test image have the same resolution.

State of the art visual quality assessment methods are effective when the input image and the reference image have the same resolution. However, estimating the quality of an image that has spatial resolution different than that of the reference image is still a challenging problem. In this work, we design a quality estimator (QE) to estimate the quality of a distorted image with a lower resolution compared to the reference image. We also present a subjective test environment to explore the success of the proposed algorithm in comparison with other QEs. The subjective tests demonstrate that the proposed method works better than other approaches.

During wireless video transmission, when an error is detected at the receiver, the packet is dropped immediately and error concealment is performed. However, this procedure does not always provide satisfactory results, especially when the motion is complicated or scene change occurs. Moreover, there are often uncorrupted macroblocks in the distorted packet. Hence, it can be important to utilize the information in the distorted packet. In this work, our aim is to explore which produces better quality: discarding or decoding the corrupted packet. First, we examine the effect of an error in each syntax element on the visual quality. Second, we compare the visual degradation caused by an error in each syntax element with that caused by discarding the whole packet. We also evaluate the effects of motion and quantization.