ISO/IEC JTC 1 SC 42 Artificial Intelligence - Working Group 4
Use Cases & Applications

Editor's comments and enhancements are shown in green. [ Reviewed]

The quality of use case submissions will be evaluated for inclusion in the Working Group's Technical Report based on the application area, relevant AI technologies, credible reference sources (see References section), and the following characteristics:

  • [1] Data Focus & Learning: Use cases for AI system which utilizes Machine Learning, and those that use a fixed a priori knowledge base.
  • [2] Level of Autonomy: Use cases demonstrating several degrees (dependent, autonomous, human/critic in the loop, etc.) of AI system autonomy.
  • [3] Verifiability & Transparency: Use cases demonstrating several types and levels of verifiability and transparency, including approaches for explainable AI, accountability, etc.
  • [4] Impact: Use cases demonstrating the impact of AI systems to society, environment, etc.
  • [5] Architecture: Use cases demonstrating several architectural paradigms for AI systems (e.g., cloud, distributed AI, crowdsourcing, swarm intelligence, etc.)
  • [6] Functional aspects, trustworthiness, and societal concerns
  • [7] AI life cycle components include acquire/process/apply.
These characteristics are identified in red in the use case.

No. 32 ID: Use Case Name: AI solution to help mobile phone to have better picture effect
Hybrid or other
StatusIn operation
ScopeBetter understanding the image and improving image effect on smartphone by using DL model which is trained in the cloud or offline.
Objective(s)To find an efficient solution to Increase camera image quality on smartphone without Increasing too much operation and power burden for mobile phone.
(up to
150 words)
An AI solution was developed that could increase smartphone camera image quality. Using deep learning, smartphone can Identify more scenarios and objects than before. Based on the identified scenarios and objects, smartphone can better understand the image and improve image effect.
Complete Description At present, there are 1.4 billion smart phone shipments in the world every year. Photography is one of the most important functions of smart phones. The industry has been trying to improve the picture quality of mobile phone photography. It hopes to reach even the quality of the professional SLR camera. The traditional image processing algorithm is currently facing the ceiling, many scenes traditional algorithms can ot be used, just because the effect is very poor.

Deep learning algorithm provides a turning point for solving the above problems. By using the AI solution, smartphones can better "understand" the pictures they take. Based on the deep learning algorithm, the smart phone can analyze the shooting scene in real time and intelligently identify various scenes in the shooting process, such as blue sky, flowers, green plants, night view, snow scene, etc. And the smart phone can also intelligently detect the shooting objects in the scene. Base on scene recognition and object detection,the smartphone can automatically adjust and set parameters for different pictures, so as to get better photo effects.
Now the mobile phone can recognize 100 kinds of scenes and can reach hundreds in the future. By using the depth learning algorithm, the mobile phone can now detect the 20 types of subjects, and the future can be detected by hundreds of subjects. Object detection can be used for SmartZoom (auto focus on targets), and portrait segmentation can be used for background blur or light efficiency.

Stakeholdersmobile phone manufacturer, end user, third party testing and evaluation agency
Assets, Values
Threats &
new privacy threats (hidden patterns).
Indicators (KPIs)
Seq. No. Name Description Reference to mentioned
use case objectives
1 MIoU (Mean Intersection over Union) The intersection of prediction area and actual area divided by the union of the predicted area and the actual area. Ideal target is 100%. Improve accuracy
2 FAR (false acceptance rate) Negative samples are identified as positive samples / Total number of negative samples.The low FAR, the more smartphone will get correct scenes and objects Improve accuracy
AI Features Task(s)Recognition
Method(s)Deep learning
HardwareNPU, GPU, CPU etc.
TopologyNo Need
Terms &
Concepts Used
Deep learning, 'Understand'
The standardized content includes:
  1. 1the format of training picture data;
  2. the format of deep learning model generated offline or cloud, which will be transplanted to smart phones;
  3. the platform to support the transplanted model in the smart phone;
  4. API which can be used by others applications, such as: picture classification, security.
& Issues
Challenges: Achieve the same level as professional SLR camera for pictures.
  1. Lack of data for certain scene;
  2. Lack of computing ability on terminal side;
  3. Users can feel the improvement of image quality, but may not know that it is brought by AI.
Societal Concerns Description For the wrong object detection, it may lead to racial prejudice or privacy protection problems
SDGs to
be achieved
Industry, Innovation, and Infrastructure
Data Characteristics
Description Annotated pictures
Source Public picture library /Self collection picture library /Web crawling pictures /Automatic synthesis of pictures
Type Picture format supported by a training platform and smart phone
Volume (size)
Variety Single source
(rate of change)
Scenario Conditions
No. Scenario
Triggering Event Pre-condition Post-Condition

Training Scenario Name:
Step No. Event Name of
Description of

Specification of training data
Scenario Name Evaluation
Step No. Event Name of
Description of

Input of Evaluation
Output of Evaluation
Scenario Name Execution
Step No. Event Name of
Description of

Input of Execution
Output of Execution
Scenario Name Retraining
Step No. Event Name of
Description of

Specification of retraining data
No. Type Reference Status Impact of
use case

  • Peer-reviewed scientific/technical publications on AI applications (e.g. [1]).
  • Patent documents describing AI solutions (e.g. [2], [3]).
  • Technical reports or presentations by renowned AI experts (e.g. [4])
  • High quality company whitepapers and presentations
  • Publicly accessible sources with sufficient detail

    This list is not exhaustive. Other credible sources may be acceptable as well.

    Examples of credible sources:

    [1] B. Du Boulay. "Artificial Intelligence as an Effective Classroom Assistant". IEEE Intelligent Systems, V 31, p.76-81. 2016.

    [2] S. Hong. "Artificial intelligence audio apparatus and operation method thereof". N US 9,948,764, Available at: 2018.

    [3] M.R. Sumner, B.J. Newendorp and R.M. Orr. "Structured dictation using intelligent automated assistants". N US 9,865,280, 2018.

    [4] J. Hendler, S. Ellis, K. McGuire, N. Negedley, A. Weinstock, M. Klawonn and D. Burns. "WATSON@RPI, Technical Project Review".
    URL: 2013