Keywords

1 Introduction

The content management in recent years has gained a lot of attention due to the fact that the communication over the internet has increased to a greater extend. The recent works show for various domains multiple practices have started for making the content delivery online. Some of the business cases like for corporate practices, the meeting and the discussions are happening over the internet to increase the reachability and effective time utilization. Many of the medical practices are also using online content managements as the treatments over the internet via video conferences are getting high popularity for making knowledge sharing over various regions of the world. Also, in the field of education, many of the universities have showcased a higher rate of learning via online contents as the learners can learn at their own pace with greater engagements. So, the adaptability of the online contents, especially the video-based contents, is the demand of the recent research and practices.

Nonetheless, preserving the video quality is always a task for various reasons. In this work, the challenges are precisely identified based on the parallel research outcomes and further, the proposed solutions as a complete framework on cloud systems are also elaborated with significant improvements over recent and parallel research outcomes.

The later sections of this research are furnished as such a way that in Section 2, the parallel outcomes are discussed; in Section 3, the problems and research scope based on the earlier research outcomes are identified; in Section 4, the proposed frameworks and the sequence of the deployed algorithms are discussed; in Section 5 the results are analyzed; in Section 6, the real-time deployment of the proposed framework is analyzed on cloud; in Section 7, the comparative analysis is carried out; and, at the end, conclusion and future research directions of this research are discussed and on Section 8.

2 Literature Review

The parallel research outcomes will help in understanding the fundamental improvements in this domain of research and, at the same time, the gaps in the research can also be identified.

The work by Feng et al. [1] has recently demonstrated the applicability of the video data processing for a higher volume of the data. The work has clearly showcased the benefits of contextual video data processing for learning specific content retrieval. The personalized video searching, and retrieval process is definitely the demand for the current research.

The video content retrieval is not only the challenge of the present research context. Rather, the security or the sharing of the video contents are also to be considered with equal importance. The work by Yang et al. [2] has showcased the need for the time-dependent video content security approaches for a higher volume of the video data. This work also demonstrates the time-dependent query processing using the historical query results for making the query processing faster.

Further, the information, specifically the video information retrieval process is complex for the cloud- or cluster-based architectures. Thus, the information of the video content distribution is one of the keyfactors to be identified for context-based searching from distributed data sources. The work by Yang et al. [3] has listed the benefits and the challenges of the cloud-based content retrieval processes.

Many of the parallel research works have demonstrated similar outcomes focusing on the content retrieval processes. The majority of the works have showcased better outcomes for the static multimedia contents such as images. The work by Wang et al. [4] has established a process for managing, storing, and retrieval actions on the video data. The CHCF framework is widely accepted by the research community.

During the retrieval of the video or image or multimedia contents, the enhancements of the contents during the searching process is also important. Thus, the noise reduction processes come into the place for many of the parallel research works. The similar outcome by Song et al. [5] for content retrieval processes have confirmed this ideology.

The video content retrieval process is used for various purposes from security to education to entertainments. The distinguished work by Gao et al. [6] has demonstrated the use of video content retrieval methods for the identification of human or other objects from the video. The similar measures can be applied for identification or retrieval of the video contents, where the video contents are the search string or search input.

Yet another direction of the research community is extending their expertise for making the complete video content retrieval process with lower time complexity. The demand is eventually propagating throughout all the aspects of the content retrieval processes, where the searching device, the device generating the search string, is limited in terms of resources and processing capabilities. The work by Liu et al. [7] has showcased the process of video content retrieval from mobility devices with novel indexing methods. The identified problem from these works is discussed in the next section of the work.

3 Problem Identification

In this segment of the research, after a thorough analysis of the recent and parallel results are examined for identifying the bottlenecks of the parallel research outcomes and propose the research objectives as shown in Table 1.

Table 1 Identification of the research objectives
Full size table

Henceforth, the final research objectives are furnished here:

  1. 1.

    Objective-1: Apply a novel solution for the reduction of the keyframes with the reduction of the time complexity and improvement of the content retrieval accuracy.

  2. 2.

    Objective-2: Apply a novel solution for the improvement of content retrieval process on cloud-based or distributed online contents with the knowledge of data distributions over clusters.

  3. 3.

    Objective-3: Apply a novel solution for providing data encryption and decryption for data at rest and data on go for the complete framework with the reduction in time complexity and improved accuracy for content retrieval.

Based on the identified problems and the proposed solutions, the conceptual framework is formulated in the next portion of this work.

4 Proposed Framework

Based on the identification of the problems, research gaps and outcomes from the earlier research works, in this section, the proposed framework is elaborated (see Fig. 1).

Fig. 1
figure 1

Proposed framework for video content retrieval and management

Full size image

The proposed algorithms are the outcomes from the works of the same authors and are summarized here.

Firstly, the Segmented Noise Removal Processing using Deep Segmentation Algorithm is displayed here [8].

Secondly, the Adaptive Threshold Calculation for Distributed Video Storage Algorithm is displayed here [8, 11].

Thirdly, the Video Data Summarization with Adjustable Threshold Correction is displayed here [9].

Fourthly, the Video Content Retrieval using Local Cluster Knowledge Discovery Factors is displayed here [9].

Fifthly, the “Key Generation Based on Quartic Polynomial Randomization Algorithm” is displayed here [10].

Sixthly, the Keyframe Similarity Region Extractor using Adaptive Progression Algorithm is displayed here [10].

Seventhly, the Time Restricted Region Encryption Algorithm is displayed here [10].

Finally, the Time Restricted Region Decryption Algorithm is displayed here [10].

The proposed framework is deployed on the cloud infrastructure and the deployment environment is elaborated in the next section of this work.

5 Results and Discussions

After the identification of the problems and the detailed discussion on the framework, the results obtained from the proposed framework is discussed (Table 2).

Table 2 Accuracy analysis
Full size table

The first outcome from the framework is nearly 96.83% accuracy for content retrieval process with mean of 95.71%, which also demonstrates the normal distribution of the accuracy for various datasets and various test runs (see Fig. 2).

Fig. 2
figure 2

Accuracy of SNRP-DS & ATC-DVS (%)

Full size image

The second outcome from the framework is nearly 97.93% accuracy for content retrieval process with mean of 97.15%, which also demonstrates the normal distribution of the accuracy for various datasets and various test runs (see Fig. 3).

Fig. 3
figure 3

Accuracy of VDS-ATC & VCR-LCKDF (%)

Full size image

The final outcome from the framework is nearly 99.93% accuracy for content retrieval process with mean of 99.41%, which also demonstrates the normal distribution of the accuracy for various datasets and various test runs (see Fig. 4).

Fig. 4
figure 4

Accuracy of KG-QPR & KFSRE-AP & TRRE & TRRD (%)

Full size image

Moreover, the incremental growth of the accuracy is also visible in the results and visualized graphically here (see Fig. 5).

Fig. 5
figure 5

Incremental improvement of accuracy analysis

Full size image

Secondly, the time complexity analysis is carried out (Table 3).

Table 3 Time complexity analysis
Full size table

It is natural to the observer that the time complexity is reducing progressively over the complete framework for three phases as nearly 9.54 s for the first phase, 7.93 s for the second phase, and, finally, 7.12 s in the final phase of the framework. The results are visualized graphically here (see Fig. 6).

Fig. 6
figure 6

Time complexity analysis

Full size image

Finally, with the detailed analysis of the obtained results, in the next section of the work, the comparative analysis is carried out.

6 Cloud-Based Deployment Models

One of the primary objectives of this research is to deploy the complete working framework on the cloud environment. Thus, this work selects the Amazon Web Services (AWS) cloud environment for the deployment of the work. The AWS framework provides multiple benefits for video content management and sharing. One the key components, which is used by the proposed framework, is the content management pipelines. The pipeline is one of the components which enables processing of the video data based on the sequences of the algorithms. The first two algorithms are deployed on the same component and the visualization is furnished here (see Fig. 7).

Fig. 7
figure 7

Deployment on AWS pipelines

Full size image

Further, the content management and retrieval algorithms can further be deployed on the jobs on AWS. The job component of the AWS enables multiple algorithms sequences to be applied on the video contents adaptively based on the pre-set sequences. The next two algorithms are deployed on the AWS and the deployment scenario is furnished here (see Fig. 8).

Fig. 8
figure 8

Deployment on AWS jobs and pre-sets

Full size image

Further, the complete framework is again managed from the AWS EC2 instances and the deployment scenario is furnished here (see Fig. 9).

Fig. 9
figure 9

Deployment on AWS EC2 instances

Full size image

Furthermore, in the next section of this work, the comparative analysis is carried out.

7 Comparative Studies

For any research outcome, to be identified as the best, it is important to perform the comparative study of parallel research findings. Henceforth, the proposed framework is contrasted with the parallel major research findings in this portion of the study (Table 4).

Table 4 Comparative analysis
Full size table

In terms of retrieval accuracy, time complexity, overall framework complexity with additional features, it is observed that the proposed method outperforming. The improvements for content retrieval accuracy is nearly 15% and the improvement over time complexity is nearly 85%.

The accuracy improvements are visualized graphically here (see Fig. 10).

Fig. 10
figure 10

Content retrieval accuracy analysis

Full size image

The improvements over time complexity are also visualized graphically as (see Fig. 11).

Fig. 11
figure 11

Time complexity analysis

Full size image

Henceforth, in the next section of the work, the final research conclusion is presented.

8 Conclusion

The motivating factor for online video-based content management is wide adaptations and popularity. This catalyzed the growth in the recent research trends for making the video content management and revivals better and faster and more secure for cloud. However, the bottlenecks identified by many other researchers and this work as well are the setbacks for the growth of more adaptation of the video content delivery and management over cloud. Henceforth, this work designed a three-phase solution of the complete problem as in the first phase, designed a novel solution for the reduction of the keyframes with reduction of the time complexity and improvement of the content retrieval accuracy of 96.83%, in the second phase, designed a novel solution for improvement of content retrieval process on cloud-based or distributed online contents with the knowledge of data distributions over clusters with an accuracy of 97.83%, and, in the final phase, designed a novel solution for providing data encryption and decryption for data at rest and data on go for the complete framework with a reduction in time complexity and improved accuracy for content retrieval with an accuracy of retrieval 99.93%. Considering the final outcome and the comparative improvements over the other parallel research outcomes, this work is to be considered as one of the benchmarked work in this domain of research for making the video content management on cloud a faster, secure, and better area.