Special Issue on Advance in Digital Signal, Image and Video Processing

It is assumed that high performance and quality, especially in the case of signal transmission, will lead to great acceptance and usability. However, with the outbreak of numerous digital signal, image and video processing systems and services, low-quality or best-effort services have gained enormous popularity. Moreover, it shows that the relationship between performance, quality and acceptance is not fully recognized. A method for identifying the scrambling types of linear block codes (LBC) by combining correlation features and convolution long short-term memory (LSTM) neural networks (NNs) is proposed in [1]. In order to improve the bit error performance while maintaining low decoding steps, the authors of [2] introduce a neural network subcode that can achieve optimal decoding performance and combine it with the traditional fast successive-cancellation (SC) decoding algorithm.

During imaging, each infrared focal plane linear array scan detection unit determines a row of pixels in the image output. Correcting nonuniformity in high-resolution images without destroying delicate details is challenging. In this paper [3], a single-frame-based nonuniformity correction algorithm is proposed. In another study [4], the requirements for image dehazing methods have been put forward in order to solve the problems associated with nighttime and daytime dehazing, whereas in [5], authors introduce an integrity preservation analysis scheme of video contents working on the blockchain systems.

As we know, content streaming represents a significant part of Internet traffic. The Next-Generation Network (NGN) architecture was proposed for delivering various multimedia services with guaranteed quality. For this reason, the elements of the IP Multimedia Subsystem (IMS) concept, an important part of 4G/5G/6G mobile networks, are used in its service stratum [6]. During the playback, a video player monitors network throughput and dynamically selects the best video quality in the given network conditions. In this work [7], authors improved the throughput estimation using prediction produced by LSTM artificial neural networks (ANNs). Preserving visual quality is a major constraint for any algorithm in image and video processing applications. Advanced Video Coding (AVC) and High-Efficiency Video Coding (HEVC) are the extensively used video coding standards for various video processing applications nowadays [8]. The results of both subjective and objective quality assessments of H.264-, H.265-, and VP9-encoded video are presented in [9].

Even with the aid of visual information, maintaining stable and reliable working conditions is a matter of vital importance for various companies, especially those involving heavy machinery. As an example, oil sheen on the water surface can indicate a source of hydrocarbon in underlying subaquatic sediments [10]. In the machining process, it is important to predict both tool cost and life, and to reduce the equipment downtime. In [11], a new model is proposed for the effective classification of both serviceable and worn cutting edges. Still, due to human exhaustion, as well as unpredicted hazards and dangerous situations, the personnel have to take actions and wisely plan each move. This paper [12] presents a human–computer interaction (HCI)-based system that uses a concentration level measurement function to increase the safety of machine and equipment operators. The goal of another study [13] was to analyze and present an in-depth account of the implementation of mixed agile-oriented tools and practices.

As has been shown, quality evaluation is focused on defining the characteristics of media transmission systems or services and their acceptance by customers, and it can be understood in many different ways. With the outbreak of desktop and mobile platforms, the number of multimedia services and highly capable consumer devices continues to grow. However, in order to safeguard one’s privacy while accessing the Internet, it is crucial to have an antivirus program installed on the device [14]. Still, the topic of digital signal, image and video processing is an important and practical subject area. Future studies will surely require a broader analysis of numerous scientific and industrial aspects.

Acknowledgments

Thanks to all the authors and peer reviewers for their valuable contributions to the Special Issue ‘Advance in Digital Signal, Image and Video Processing’.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

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References

1. Tan, J.; Zhang, L.; Zhong, Z. Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features. Appl. Sci.; 2022; 12, 11305. [DOI: https://dx.doi.org/10.3390/app122111305]

2. Liu, H.; Zhang, L.; Yan, W.; Ling, Q. Neural-Network-Assisted Polar Code Decoding Schemes. Appl. Sci.; 2022; 12, 12700. [DOI: https://dx.doi.org/10.3390/app122412700]

3. Li, B.; Chen, W.; Zhang, Y. A Nonuniformity Correction Method Based on 1D Guided Filtering and Linear Fitting for High-Resolution Infrared Scan Images. Appl. Sci.; 2023; 13, 3890. [DOI: https://dx.doi.org/10.3390/app13063890]

4. Tang, Y.; Xiang, Y.; Chen, G. A Nighttime and Daytime Single-Image Dehazing Method. Appl. Sci.; 2023; 13, 255. [DOI: https://dx.doi.org/10.3390/app13010255]

5. Lee, W.Y.; Choi, Y.-S. Reliable Integrity Preservation Analysis of Video Contents with Support of Blockchain Systems. Appl. Sci.; 2022; 12, 10280. [DOI: https://dx.doi.org/10.3390/app122010280]

6. Kaczmarek, S.; Sac, M. Performance Evaluation of a Multidomain IMS/NGN Network Including Service and Transport Stratum. Appl. Sci.; 2022; 12, 11643. [DOI: https://dx.doi.org/10.3390/app122211643]

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8. Christopher, P.R.; Sathasivam, S. Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265. Appl. Sci.; 2022; 12, 12952. [DOI: https://dx.doi.org/10.3390/app122412952]

9. Klink, J.; Brachmański, S.; Łuczyński, M. Assessment of the Quality of Video Sequences Performed by Viewers at Home and in the Laboratory. Appl. Sci.; 2023; 13, 5025. [DOI: https://dx.doi.org/10.3390/app13085025]

10. Dong, J.; Sitler, K.; Scalia, J.; Ge, Y.; Bireta, P.; Sihota, N.; Hoelen, T.P.; Lowry, G.V. Application of Transfer Learning and Convolutional Neural Networks for Autonomous Oil Sheen Monitoring. Appl. Sci.; 2022; 12, 8865. [DOI: https://dx.doi.org/10.3390/app12178865]

11. David, L.G.; Patra, R.K.; Falkowski-Gilski, P.; Divakarachari, P.B.; Antony Marcilin, L.J. Tool Wear Monitoring Using Improved Dragonfly Optimization Algorithm and Deep Belief Network. Appl. Sci.; 2022; 12, 8130. [DOI: https://dx.doi.org/10.3390/app12168130]

12. Jagoda, J.; Woszczyński, M.; Polnik, B.; Falkowski-Gilski, P. HCI-Based Wireless System for Measuring the Concentration of Mining Machinery and Equipment Operators. Appl. Sci.; 2023; 13, 5396. [DOI: https://dx.doi.org/10.3390/app13095396]

13. Weichbroth, P. A Case Study on Implementing Agile Techniques and Practices: Rationale, Benefits, Barriers and Business Implications for Hardware Development. Appl. Sci.; 2022; 12, 8457. [DOI: https://dx.doi.org/10.3390/app12178457]

14. Samociuk, D. Antivirus Evasion Methods in Modern Operating Systems. Appl. Sci.; 2023; 13, 5083. [DOI: https://dx.doi.org/10.3390/app13085083]