How to Measure the Quality of AIGC Services

Table of Links

Abstract and 1. Introduction

1.1 Background

1.2 Motivation

1.3 Our Work and Contributions and 1.4 Organization

2. Related Work

   2.1 Mobile AIGC and Its QoE Modeling

   2.2 Blockchain for Mobile Networks

3. Preliminaries

4. Prosecutor Design

   4.1 Architecture Overview

   4.2 Reputation Roll-up

   4.3 Duplex Transfer Channel

5. OS2a: Objective Service Assessment for Mobile AIGC

   5.1 Inspiration from DCM

   5.2 Objective Quality of the Service Process

   5.3 Subjective Experience of AIGC Outputs

6. OS2A on Prosecutor: Two-Phase Interaction for Mobile AIGC

   6.1 MASP Selection by Reputation

   6.2 Contract Theoretic Payment Scheme

7. Implementation and Evaluation

   7.1 Implementation and Experimental Setup

   7.2 Prosecutor Performance Evaluation

   7.3 Investigation of Functional Goals

   7.4 Security Analysis

8. Conclusion and References

5.2 Objective Quality of the Service Process

The objective quality of the AIGC service process can be evaluated by various KPIs from different perspectives. Service latency is one of the most critical concerns because, 8 given the immature AIGC governance, the currently common practice is that the client who first publishes one AIGC output owns the copyright [46]. Consequently, clients will always expect a service latency as small as possible to prevent similar ideas from being published by other mobile neighbors. Without loss of generality, in this paper, we take AIGC service latency as an example to showcase the OS2AO calculation.

5.2.1 Modeling of Channel Establishment

5.2.2 Generalization to Other KPIs

5.3 Subjective Experience of AIGC Outputs

Besides the objective KPIs, clients also care about whether the generated AIGC outputs can meet their personal requirements, which we coin as the experiences of the AIGC service outcome. Considering the subjectivity in artwork assessment, clients evaluate the AIGC outputs using their own standards and preferences. Nonetheless, regardless of which kind of model is used, the final output should be converted to a boolean judgment, whose value is 0 or 1. Value 1 means that the client receives a satisfying AIGC output. In contrast, value 0 indicates that the received output suffers from flaws, e.g., low resolution, distortion, and mismatched art style. Then, an intuitive way is to average the judgment items from all the clients and calculate the reputation score for every MASP. However, recall that these clients hold different levels of strictness. We should calibrate the judgment from numerous heterogeneous clients and generate a fair overall reputation for each MASP. To reach this goal, we use WMSL [16] to calibrate the judgment, as shown in Algorithm 2.

5.3.1 Local Opinion

5.3.2 Reference Opinion and Calibration

5.3.3 Opinion Fusion and Reputation

5.3.4 Discussion: Rationale of Reputation

[3] Data available at http://xblock.pro/#/dataset/14