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Over the past few years, there has been substantial interest and research activity surrounding the application of Convolutional Neural Networks (CNNs) for post-filtering in video coding. Most current research efforts have focused on using CNNs with various kernel sizes for post-filtering, primarily concentrating on High-Efficiency Video Coding/H.265 (HEVC) and Versatile Video Coding/H.266 (VVC). This narrow focus has limited the exploration and application of these techniques to other video coding standards such as AV1, developed by the Alliance for Open Media, which offers excellent compression efficiency, reducing bandwidth usage and improving video quality, making it highly attractive for modern streaming and media applications. This paper introduces a novel approach that extends beyond traditional CNN methods by integrating three different self-attention layers into the CNN framework. Applied to the AV1 codec, the proposed method significantly improves video quality by incorporating these distinct self-attention layers. This enhancement demonstrates the potential of self-attention mechanisms to revolutionize post-filtering techniques in video coding beyond the limitations of convolution-based methods. The experimental results show that the proposed network achieves an average BD-rate reduction of 10.40% for the Luma component and 19.22% and 16.52% for the Chroma components compared to the AV1 anchor. Visual quality assessments further validated the effectiveness of our approach, showcasing substantial artifact reduction and detail enhancement in videos. [ABSTRACT FROM AUTHOR]

A deblocking filtering method that reduces the blocking artifacts that result from the use of the illumination change-adaptive motion compensation method, which was already adopted in the current multiview video coding (MVC) standard, is introduced. When the macroblock (MB)-based illumination compensation method is used to compensate local illumination changes in multiview video sequences, the neighboring MBs could have different illumination values that represent the average values of the luminance MBs, horizontally and vertically. This phenomenon causes horizontal/vertical blocking artifacts on the MB boundaries due to the difference in illumination between the neighboring MBs. The proposed deblocking filtering method is designed to reduce this phenomenon by using the adequately modified boundary strength derivation process of the current H.264/AVC deblocking filtering mechanism. The proposed deblocking filtering method improves the subjective video quality and provides slightly better objective video quality in multiview video sequences. Index Terms--Deblocking filter, H.264/AVC, illumination compensation, multiview video coding (MVC).

In the content-based image coding scheme, segmentation information of the arbitrarily shaped regions may be available for both encoder and decoder. The shape-assisted block-based texture coding methodologies, such as shape-adaptive discrete cosine transform (SADCT), can use this segmentation information to improve coding efficiency. In this paper, we introduce the shape-adaptive region partitioning (SARP) methods which can reduce the number of coding blocks that partition the arbitrarily shaped region by modifying the block positions. By simply adding SARP method to aid the SADCT, the coded texture bits can be reduced by 5-10%, in comparison with the SADCT using common block-based coding infrastructure which is usually used in the MPEG-1/2 and H.263. Index Terms - Multidimensional coding, multimedia systems, video signal processing.

Optical ellipsometry has been found to be a promising technique for monitoring process parameters, such as film composition and film thickness, of semiconductor wafers grown with molecular beam epitaxy. Whereas it is a straightforward task to calculate ellipsometry angles given the thickness of the film and the refractive indexes of the film and substrate, it is a difficult task to invert that mathematical relationship. However, the process must be inverted if the measured parameters are to be interpreted meaningfully in terms of film composition and film thickness. This paper reports on the use of neural-net computing for the inverse mapping of measured ellipsometry parameters. We used a functional-link net which is very efficient in function approximation. The advantage of using the net, however, is not only its speed, but also because some other net architecture characteristics allow us to perform the task in a holistic manner.

Recently, social media have been used by researchers to detect depressive symptoms in individuals using linguistic data from users' posts. In this study, we propose a framework to identify social information as a significant predictor of depression. Using the proposed framework, we develop an application called the Socially Mediated Patient Portal (SMPP), which detects depression-related markers in Facebook users by applying a data-driven approach with machine learning classification techniques. We examined a data set of 4350 users who were evaluated for depression using the Center for Epidemiological Studies Depression (CES-D) scale. From this analysis, we identified a set of features that can distinguish between individuals with and without depression. Finally, we identified the dominant features that adequately assess individuals with and without depression on social media. The model trained on these features will be helpful to physicians in diagnosing mental diseases and psychiatrists in analysing patient behaviour. [ABSTRACT FROM AUTHOR]