Objective Video Quality Assessment and Ground Truth Coordinates for Automatic License Plate Recognition^†

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

1.      This work builds a novel methodology for an objective model aimed at evaluating video quality specifically for automatic license plate recognition tasks. To provide robust evaluation framework evaluations indicate that the proposed model scores highly on the F 16 measure, with a value of 0.777, suggesting that it offers a promising approach to evaluate video quality in 17 tasks that involve recognition of objects or license plates.

2.      Authors should review the journal paper format.

3.      The author topic is original, but he should discuss more in the introduction section about the gap in the Target Recognition Videos (TRVs) today he talks that’s a problem now but not talk about it in detail.

4.      Authors build their own datasets and apply their model on this dataset. He didn’t use a benchmark dataset and didn’t compare with others’ results. He can discuss this and talk about the available datasets for this topic.

5.      Authors cited his previous paper conference paper in this paper they implemented their new algorithm and show the algorithm more detailed in this paper they gain high accuracy.

6.      The author can try his model and solve the assumption is that an experiment iteration cannot last more than a week to increase the time for more images in his methodology.

7.      At the section 2 : This text file adheres to the following naming convention line number : 122 video_name_anno.txt at this section author must add the date he use this datasets

8.      Author should update figure 2 to be clearer.

9.      Figure 3 and figure 4 not suitable to the journal doc formatting size should be updated.

10.   From line 187 to 193 the numbering should be arranged in the journal doc formatting

11.   All tables format and size not formatting according to the journal doc formatting.

12.   Figure 6 in section Overview of the Recognition Experiment must be discussed more in detail and the words are too small and not readable so us must change the font size.

13.   You should clear all space area in figure 12 and 14.

14.   Figure 15 not clear words too small

15.   From page 22 to 30 authors should delete all free spaces between tables and words

16.   Authors should talk about the results at figure 24 and must be added before the conclusion section.

17.   authors should add a future work in this paper

Comments on the Quality of English Language

author should review the english of the paper again

Author Response

• 1. This work builds a novel methodology for an objective model aimed at evaluating video quality specifically for automatic license plate recognition tasks. To provide robust evaluation framework evaluations indicate that the proposed model scores highly on the F 16 measure, with a value of 0.777, suggesting that it offers a promising approach to evaluate video quality in 17 tasks that involve recognition of objects or license plates.

We are grateful for your positive evaluation of our work and for acknowledging the novelty of our methodology for video quality assessment, specifically in the context of automatic license plate recognition tasks. Your recognition of the promising results indicated by the high F-measure value is highly encouraging. It reaffirms our belief that the proposed model can significantly contribute to the field by providing a robust framework for evaluating video quality in object and license plate recognition scenarios. We appreciate your insightful comments and look forward to further discussing the impact and potential applications of our research.

• 2. Authors should review the journal paper format.

Thank you for drawing our attention to the formatting inconsistencies in our manuscript with respect to the journal's guidelines. We would like to inform you that we have meticulously reviewed the entire paper and have made all necessary corrections. These adjustments were made in accordance with the MDPI template for LaTeX, which we have employed throughout the writing process. We have ensured that all elements of the paper now correctly follow the prescribed format. We appreciate your patience and guidance in this matter and believe that these revisions have enhanced the presentation and readability of our work.

• 3. The author topic is original, but he should discuss more in the introduction section about the gap in the Target Recognition Videos (TRVs) today he talks that’s a problem now but not talk about it in detail.

We greatly appreciate your recognition of the originality of our topic and your constructive feedback regarding the introduction section of our paper. Following your suggestion, we have revised the introduction to include a more detailed discussion about the current gaps in Target Recognition Videos (TRVs). Specifically, we have elaborated on the challenges faced by existing quality predictors, highlighting their shortcomings in handling critical conditions such as variable lighting and motion blur. We have also addressed how these limitations affect the practical utility of TRVs in real-world scenarios. We believe that these additions will provide readers with a clearer understanding of the problem space and further emphasise the significance of our research in addressing these issues.

• 4. Authors build their own datasets and apply their model on this dataset. He didn’t use a benchmark dataset and didn’t compare with others’ results. He can discuss this and talk about the available datasets for this topic.

Thank you for your comments concerning our use of a custom dataset and the lack of comparison with benchmark datasets. In the revised manuscript, we have acknowledged the existence of established benchmark datasets in the field of Target Recognition Videos (TRVs). Moreover, we have included a justification for the creation of our own dataset, which has been specifically tailored to meet the unique requirements of our study. We have also explained how our custom dataset addresses certain challenges that are not covered by existing benchmarks, thereby reinforcing the relevance and necessity of our approach to the research question at hand. We believe this thorough discourse will clarify the rationale behind our methodological choices and contribute to a more comprehensive understanding of the work presented.

• 5. Authors cited his previous paper conference paper in this paper they implemented their new algorithm and show the algorithm more detailed in this paper they gain high accuracy.

Regarding the citation of our previous conference paper, we would like to clarify that the cited paper was a preliminary short-paper of the current work, providing an initial overview without detailed analysis or a comprehensive ground-truth database. The current paper significantly extends the initial study by offering a comprehensive analysis and expanded results using a detailed ground-truth dataset. However, it's important to note that this paper does not introduce a new algorithm, but rather provides a deeper and more thorough examination of the methodology originally presented. Unlike the initial short-paper, this work includes extensive experiments, detailed result analysis, and discussions on the effectiveness and efficiency of the existing approach.

• 6. The author can try his model and solve the assumption is that an experiment iteration cannot last more than a week to increase the time for more images in his methodology.

Thank you for your suggestion to enhance our experimental methodology. We appreciate your insight into extending the time frame of our experiment iterations. It's important to note, however, that the algorithm discussed in this paper remains the same as in our previous work, as it is a continuation and a more in-depth analysis of the previously presented methodology rather than the introduction of a new algorithm. The current experimental setup, including the iteration timeframe, has been designed to balance thoroughness with feasibility, ensuring that each iteration is comprehensive yet manageable within the constraints of our resources. We believe that extending the time frame beyond a week may not yield significant additional insights for this particular study, but we acknowledge the potential value of this approach for future work.

• 7. At the section 2 : This text file adheres to the following naming convention line number : 122 video_name_anno.txt at this section author must add the date he use this datasets

Thank you for your valuable feedback regarding the need to specify the date of dataset usage in Section 2. As per your suggestion, we have now included the date of annotation creation in the "Ground Truth Annotation" paragraph. The annotations for the ALPR data set were compiled in July 2019, which we have now clearly stated in the manuscript. We believe this addition provides greater clarity on the temporal context of the dataset and enhances the transparency of our research methodology.

• 8. Author should update figure 2 to be clearer.

Thank you for your feedback on the clarity of Figure 2. We have carefully revised the figure to enhance its resolution and legibility. In the updated version, we have improved the visual elements to ensure that the figure is clear and easily interpretable by the reader. These amendments have been made to better convey the intended information and to align with the high standards of presentation expected for the journal.

• 9. Figure 3 and figure 4 not suitable to the journal doc formatting size should be updated.

We appreciate your attention to detail in pointing out the formatting issues with Figures 3 and 4. We have taken steps to ensure that all figures adhere to the journal's formatting guidelines. Upon reevaluation, we discovered that Figures 3 and 4 did not meet the journal's size specifications when using the MDPI LaTeX template. We have since resized these figures to comply with the journal's document formatting standards. We are confident that these revisions have improved the fit and integration of the figures within the manuscript, and we have ensured that all elements are now correctly formatted.

• 10. From line 187 to 193 the numbering should be arranged in the journal doc formatting

Thank you for bringing to our attention the numbering issue in lines 187 to 193 of our manuscript. After a thorough review, we discovered that the formatting did not adhere strictly to the MDPI template guidelines. We have now rectified this by reformatting the numbering to be consistent with the journal's documentation standards. We have ensured that the entire manuscript, including the sections mentioned, is now correctly formatted according to the journal's style.

• 11. All tables format and size not formatting according to the journal doc formatting.

Thank you for pointing out the discrepancies with the format and size of the tables in our manuscript. In response to your feedback, we have conducted a comprehensive review and subsequently updated all tables to align with the journal's formatting guidelines as specified in the MDPI template. These modifications have been meticulously implemented to ensure that each table is presented with the correct format and size, thereby improving the overall consistency and professionalism of the document.

• 12. Figure 6 in section Overview of the Recognition Experiment must be discussed more in detail and the words are too small and not readable so us must change the font size.

Thank you for your valuable feedback regarding Figure 6 in the "Overview of the Recognition Experiment" section. Following your suggestions, we have made necessary adjustments to improve its readability. The font size on Figure 6 has been increased to ensure the text is easily legible. Additionally, we have revised the figure itself to ensure that the details are more clearly presented and understandable, which we believe eliminates the need for a more detailed discussion in the text of the section. We trust that these modifications will significantly enhance the clarity and usefulness of Figure 6 for our readers.

• 13. You should clear all space area in figure 12 and 14.

Thank you for your comments regarding Figures 12 and 14. We have understood your recommendations regarding the removal of unnecessary blank spaces in these figures. Appropriate formatting adjustments have been made in LaTeX to enhance the aesthetics and readability of these figures. We believe that these corrections meet your expectations and will improve the overall presentation of the results in our manuscript.

• 14. Figure 15 not clear words too small

Thank you for pointing out the readability issue with Figure 15. Following your suggestion, we have made necessary adjustments to enhance its clarity. Specifically, we have increased the font size in Figure 15 to ensure that the text is easily legible. We trust that this modification will significantly improve the readability and overall effectiveness of the figure in conveying the intended information.

• 15. From page 22 to 30 authors should delete all free spaces between tables and words

Thank you for your valuable feedback on the formatting of our manuscript, specifically regarding the spacing between tables and text from page 22 to 30. We have carefully reviewed these sections and made the necessary adjustments to eliminate all superfluous spaces, ensuring a more streamlined and professional layout. These revisions have been implemented to enhance the readability and overall aesthetic of the manuscript. We appreciate your attention to detail and believe that these improvements significantly contribute to the quality of our presentation.

• 16. Authors should talk about the results at figure 24 and must be added before the conclusion section.

Thank you for your constructive comment regarding the discussion of the results depicted in Figure 24. We have expanded our analysis in the manuscript to provide a deeper insight into the error sensitivity of the model across different HRCs. Furthermore, we have adjusted the placement of Figure 24 to precede the conclusion section as suggested. This change allows for a more logical flow and better contextualization of the results within the overall narrative of the study. We appreciate your guidance in enhancing the clarity and coherence of our paper.

• 17. authors should add a future work in this paper

Thank you for your suggestion to include a section on future work in our paper. We have added a paragraph at the end of the "Conclusions" section that outlines our forthcoming research endeavours. This addition elaborates on the next steps we plan to take in refining the Just-Noticeable Difference (JND) threshold for lossless Computer Vision (CV)performance and developing a comprehensive objective quality model. We believe this inclusion provides a clear direction for how our research will proceed and sets the stage for future contributions to the field.

• author should review the english of the paper again

Thank you for your recommendation to review the English language usage in our paper. We have thoroughly re-examined and revised the manuscript to ensure clarity and correctness of the language throughout. We have addressed grammatical, spelling, and syntax issues to meet the high standards expected for publication. We believe that these revisions have significantly improved the readability of our paper.

Reviewer 2 Report

Comments and Suggestions for Authors

The authors presented a novel framework for assessing the quality of videos that are specifically aimed at license plate recognition tasks. They have factored in multiple scenarios of quality degradation in a digital camera image acquisition model. Their work is based on the OpenALPR library. A detailed analysis of the obtained results helps validate their claim.

While I have found their research work satisfactory for publication. 

Author Response

• The authors presented a novel framework for assessing the quality of videos that are specifically aimed at license plate recognition tasks. They have factored in multiple scenarios of quality degradation in a digital camera image acquisition model. Their work is based on the OpenALPR library. A detailed analysis of the obtained results helps validate their claim.

• While I have found their research work satisfactory for publication. 

We are truly grateful for your assessment of our work and your acknowledgment of the novelty of our framework for video quality assessment in license plate recognition tasks. Your recognition of the comprehensiveness of our approach and the depth of our analysis is highly appreciated. It is gratifying to hear that our work meets the standards of satisfaction for publication in your esteemed journal. We thank you for your constructive and positive feedback.

Reviewer 3 Report

Comments and Suggestions for Authors

Abstract:

The abstract looks too general and key findings are missing. It is just like and introductory paragraph. Please update the abstract.

Introduction:

This section looks fine but the contributions look very poor in presentation. Always use anonymous style, this manuscriprt covers..... this manuscript offers.... etc. "We" should be avoided.

Line 53-67: Unnecessary paragraphing has been done that looks very asymmetric and without any justification.

Methodology:

It looks fine but the addition of the general methodology flow chart will enhance the clarity.

 

 

Comments on the Quality of English Language

Minor editing of English language required

Author Response

• Abstract:

• The abstract looks too general and key findings are missing. It is just like an introductory paragraph. Please update the abstract.

We appreciate your constructive feedback regarding the abstract of our manuscript. In response, we have revised the abstract to provide a more detailed and structured overview of our work, in line with your suggestions. The revised abstract now includes specific parts on background, methods, results, and conclusions, offering a clearer and more concise summary of the key findings and the significance of our study. We trust that these changes adequately address your concerns and enhance the readability and informativeness of the abstract.

• Introduction:

• This section looks fine but the contributions look very poor in presentation. Always use anonymous style, this manuscript covers..... this manuscript offers.... etc. "We" should be avoided.

Thank you for your feedback on the presentation of the contributions in the introduction section of our manuscript. We have revised the text to adopt an impersonal style as per the journal guidelines, ensuring that the manuscript maintains an objective and professional tone throughout. The updated introduction now avoids the first-person narrative and provides a clearer statement of the paper's contributions and its distinctive approach to TRV quality assessment. We trust that these revisions address your concerns and enhance the manuscript's alignment with the journal's standards.

• Line 53-67: Unnecessary paragraphing has been done that looks very asymmetric and without any justification.

We acknowledge your observation regarding the paragraphing in lines 53-67 of our manuscript and appreciate your guidance on improving the presentation. We have revised this section by merging the paragraphs to form a single, coherent paragraph that maintains a logical flow of ideas without unnecessary breaks. This edit has been made to ensure a symmetrical and well-justified structure, as recommended by the journal's guidelines. We hope that this modification meets the expectations for a more polished and professional manuscript.

• Methodology:

• It looks fine but the addition of the general methodology flow chart will enhance the clarity.

Thank you for your valuable feedback on our manuscript. We appreciate your suggestion to include a general methodology flow chart to enhance the clarity of our paper. We have incorporated a comprehensive flow chart that visually represents our methodology. This chart effectively illustrates the integration of data from both the quality and recognition experiments, which are foundational to our approach. Our methodology, as outlined in Section “Materials & Methods” of the manuscript, involves leveraging data from Source Reference Circuits (SRCs) and applying various Hypothetical Reference Circuits (HRCs) to introduce distinct degradations. The resultant video sequences are then evaluated using an automatic license plate recognition system (ALPR) in conjunction with a Video Quality Indicator(VQI). This process forms the basis of our unique quality model, particularly effective in scenarios without a specific identification target. The new flow chart, now part of the manuscript, provides a clear and concise representation of this methodology. It delineates the steps of the SRC and HRC application to the final model formation, ensuring a better understanding of our unique approach. We believe this addition significantly enhances the clarity of our research methodology and thank you for guiding us to make this improvement.

• Minor editing of English language required

Thank you for your recommendation to review the English language usage in our paper. We have thoroughly re-examined and revised the manuscript to ensure clarity and correctness of the language throughout. We have addressed grammatical, spelling, and syntax issues to meet the high standards expected for publication. We believe that these revisions have significantly improved the readability of our paper.

Reviewer 4 Report

Comments and Suggestions for Authors

This work seems to have been carefully completed and gave some detailed results. However, the manuscript still contains many minor points and needs to be revised before it is finally published. 

1.       Abstract：How to reflect the model advancement? Is there any comparison with previous work?

2.       In the introduction, the literature is not fully organized, the author needs to supplement the relevant content and references.

3.        List of acronyms must be provided before introductions.

 

4.       It is understood that license plate recognition is already a relatively mature technology, so what is the contribution of the manuscript?

Author Response

• This work seems to have been carefully completed and gave some detailed results. However, the manuscript still contains many minor points and needs to be revised before it is finally published. 

We sincerely appreciate your acknowledgment of the meticulous efforts put into our work and the detailed results it has yielded. We understand that achieving the highest standards of academic publishing requires attention to even the most minor details. Therefore, we welcome your guidance and are fully committed to revising the manuscript to address all minor points that you or the journal may find necessary for clarification or improvement. Thank you for your constructive feedback, which is invaluable to the refinement process of our manuscript.

• 1. Abstract：How to reflect the model advancement? Is there any comparison with previous work?

We appreciate your query regarding the advancement of our model and its comparative analysis with previous work. In response to your comment, we acknowledge that the core algorithm employed in our study is consistent with that used in our previous conference paper. The advancement we present in this paper is not in the development of a new algorithm, but in the comprehensive and rigorous analysis of the existing algorithm within the specific domain of automatic license plate recognition (ALPR). The current work builds upon the preliminary findings by including extensive experiments, a detailed results analysis, and a discussion on the efficacy of the model, which was not part of the initial short paper. We have also introduced an expanded ground-truth dataset that enables a more robust evaluation of the model's performance in ALPR tasks. This allows for a deeper understanding of the model's capabilities and improvements over previous applications in practical scenarios. We revised the abstract to more accurately reflect the advancements in the application and analysis of the model, and how these contribute to the field of ALPR, rather than the development of new algorithmic approaches.

• 2. In the introduction, the literature is not fully organised, the author needs to supplement the relevant content and references.

Thank you for your valuable comment on the introduction of our paper. In accordance with your feedback, we have carefully reviewed and supplemented the literature to ensure that our references comprehensively cover the relevant content. We have incorporated additional references that not only fortify our arguments but also provide a broader context for our research within the current body of work in the field. These enhancements to the literature review serve to situate our study within the larger academic dialogue and to substantiate the necessity and significance of our contribution to the domain of target recognition videos (TRVs). We are confident that this thorough inclusion of pertinent literature enriches the introduction and addresses your recommendation. We appreciate your guidance and are open to any further suggestions you may have regarding additional literature that could strengthen our paper.

• 3 List of acronyms must be provided before introductions.

Thank you for your attention to detail in your review of our manuscript. In line with the guidelines provided by MDPI, we have included the list of acronyms in the final section of our manuscript to maintain consistency with the journal's formatting requirements. We believe that placing this list at the end allows for a smoother flow of the introductory content and ensures that readers can easily refer to the abbreviations as they become relevant in the text. We appreciate your understanding and are happy to make further adjustments should there be additional guidelines from the journal on this matter.

• 4. It is understood that license plate recognition is already a relatively mature technology, so what is the contribution of the manuscript?

Thank you for your observation regarding the maturity of license plate recognition technology. We agree that automatic license plate recognition (ALPR) has indeed reached a significant level of technological advancement. However, the effectiveness of ALPR systems is highly contingent upon the quality of the input image. The primary contribution of our manuscript is the development of a framework that evaluates the impact of image quality on ALPR performance, specifically addressing how variations in video quality can affect recognition accuracy. Our work focuses on the often-overlooked aspect of ALPR systems - the dependency on the quality of the captured image, which does not always come from optimal conditions. We provide an in-depth analysis of how different levels of degradation, such as blurring, exposure variations, and compression artifacts, influence the reliability of ALPR. The manuscript presents a novel evaluation methodology to ensure that ALPR systems maintain high accuracy even with images that deviate from the“perfect quality” often assumed in ideal conditions. This contribution is of particular relevance given the real-world scenarios where ALPR systems must operate reliably, such as in varying weather conditions and during different times of the day. We believe that this focus on the robustness of ALPR systems under diverse and non-ideal imaging conditions constitutes a significant contribution to the field, ensuring that ALPR technology can be effectively implemented in a broader range of applications and environments.

Reviewer 5 Report

Comments and Suggestions for Authors

Advantages.

Good practical research in the field of video sequence control systems for recognition tasks.  The experimental part is impressive. The topic of this article is interesting and meaningful for precise prediction and control of video quality. 

The design of the manuscript is well structured:

-        Introduction part is given.

-        The methodology part with algorithms and data is given (Materials and Methods).

-        Experimental results and analysis part is given.

-        Conclusion part is given.

-        References to literature, figures, and tables are correct.

There are no significant criticisms about the research methodology.

 

Disadvantages:

Some comments:

-        The table caption should be above the table: tables 3-4 and 11-14.

-        Delete the empty rows above the tables 7-12.

-        The list of references contains very old-time viewing sources (for example, line 667 – 23 May 2019).

 

The conclusion part should be supplemented with future research directions.

I found problems with self-citation. In the current article, one of the authors has 10 self-citations (40%).

 

 

Additional comments:

 

1.     What is the main question addressed by the research?

The main question is how to correctly solve the problem of assessing the overall Quality of Experience in modern video processing systems.

 

2.     Do you consider the topic original or relevant in the field? Does it address a specific gap in the field?

This research is original and solves the gap in the field of Quality of Experience in modern video.

 

3.     What does it add to the subject area compared with other published material?

Authors introduced a novel objective evaluation methodology tailored for Target Recognition

Videos, that address a significant gap in the nowadays literature.

 

4.     What specific improvements should the authors consider regarding the methodology? What further controls should be considered?

There are no significant criticisms about the research methodology.

 

 

 

5.     Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed?

The conclusions are general and it is necessary to specify the compliance with the key contributions given in the introduction. The conclusion part should be supplemented with future research directions.

 

6.     Are the references appropriate?

Literature references are correct. The problem with self-citation is founded. In the current article, one of the authors has 10 self-citations (40%).

 

7. Please include any additional comments on the tables and figures.

 

Figures and tables should be re-checked according to journal requirements.

Author Response

• Advantages.

• Good practical research in the field of video sequence control systems for recognition tasks.  The experimental part is impressive. The topic of this article is interesting and meaningful for precise prediction and control of video quality.  

• The design of the manuscript is well structured:

• -        Introduction part is given.

• -        The methodology part with algorithms and data is given (Materials and Methods).

• -        Experimental results and analysis part is given.

• -        Conclusion part is given.

• -        References to literature, figures, and tables are correct.

• There are no significant criticisms about the research methodology.

Thank you for your positive feedback on our manuscript. We appreciate your recognition of our work's practical significance and methodological rigor. Your comments encourage us to continue our research.

• Disadvantages:

• Some comments:

• -        The table caption should be above the table: tables 3-4 and 11-14.

Thank you for your meticulous review and for pointing out the placement of the captions for tables 3-4 and 11-14. We have revised the manuscript and adjusted the table captions to be positioned above the tables, adhering to the guidelines provided in the MDPI LaTeX template. We appreciate your guidance which has helped enhance the clarity and presentation of our work.

• -        Delete the empty rows above the tables 7-12.

Thank you for your observation regarding the empty rows above tables 7-12. We have addressed this issue by removing the unnecessary space, enhancing the overall layout and presentation as per the MDPI LaTeX template formatting rules. Your feedback is invaluable in assisting us to refine the manuscript.

• -        The list of references contains very old-time viewing sources (for example, line 667 – 23 May 2019).

Thank you for your comment regarding the references in our manuscript. We acknowledge the importance of citing up-to-date sources and have made a concerted effort to update the majority of our references to include more recent publications. However, we also recognize that certain topics have not been further developed in the literature since the sources we cited, which explains the inclusion of some references that are older than a decade. These older references represent foundational work or specific cases where no recent literature is available to provide updated insights. We believe they still hold relevance to the context of our research and contribute to the comprehensiveness of the discussion. 

• The conclusion part should be supplemented with future research directions.

Thank you for your suggestion to include a section on future work in our paper. We have added a paragraph at the end of the "Conclusions" section that outlines our forthcoming research endeavours. This addition elaborates on the next steps we plan to take in refining the Just-Noticeable Difference (JND) threshold for lossless Computer Vision (CV)performance and developing a comprehensive objective quality model. We believe this inclusion provides a clear direction for how our research will proceed and sets the stage for future contributions to the field.

• I found problems with self-citation. In the current article, one of the authors has 10 self-citations (40%).

We appreciate your observations regarding the self-citation rate in our manuscript. Following your recommendations, we have carefully reviewed our citation practices and taken steps to ensure a more balanced representation of the broader scholarly work related to our research. We have added new references that contribute significantly to the context and discussions within our paper. Concurrently, we have judiciously removed certain self-citations that were not critical to the understanding of our current work. As a result of these adjustments, we are pleased to report that the self-citation rate now stands at 14%. We trust that this modification maintains the integrity and academic rigour of our article while adhering to the citation guidelines.

• Additional comments:

• 1.     What is the main question addressed by the research?

• The main question is how to correctly solve the problem of assessing the overall Quality of Experience in modern video processing systems.

• 2.     Do you consider the topic original or relevant in the field? Does it address a specific gap in the field?

• This research is original and solves the gap in the field of Quality of Experience in modern video.

• 3.     What does it add to the subject area compared with other published material?

• Authors introduced a novel objective evaluation methodology tailored for Target Recognition Videos, that address a significant gap in the nowadays literature.

• 4.     What specific improvements should the authors consider regarding the methodology? What further controls should be considered?

• There are no significant criticisms about the research methodology.

Thank you for acknowledging the originality and significance of our work in the field of Quality of Experience for video processing systems. We are pleased that our methodology for Target Recognition Videos has been positively received. We appreciate your constructive comments.

• 5.     Are the conclusions consistent with the evidence and arguments presented and do they address the main question posed?

• The conclusions are general and it is necessary to specify the compliance with the key contributions given in the introduction. The conclusion part should be supplemented with future research directions.

We appreciate your constructive feedback on our manuscript. As suggested, we have refined our "Conclusions" section to better align with the key contributions outlined in the "Introduction". We have also expanded on the implications of our findings and outlined directions for future research. Please find the revised section within the revised version of the manuscript.

• 6.     Are the references appropriate?

• Literature references are correct. The problem with self-citation is founded. In the current article, one of the authors has 10 self-citations (40%).

Thank you for reiterating your concern regarding self-citations. As previously addressed, we have revised the manuscript to lower the self-citation rate to 14%, ensuring compliance with the journal's guidelines. We believe the current reference list accurately reflects the research landscape and supports the manuscript's content effectively.

• 7. Please include any additional comments on the tables and figures.

• Figures and tables should be re-checked according to journal requirements.

Thank you for your attention to the details concerning our tables and figures. Following your suggestion, we have thoroughly re-examined each figure and table to ensure compliance with the journal’s formatting requirements. Any necessary adjustments have been made to align with the specified guidelines. We appreciate your guidance in this matter and have endeavored to meet the high standards of presentation that the journal upholds. We trust that the revised manuscript now fully adheres to the required formatting norms.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

authors updated all required thanks