Category All posts

Geneva, 30 September 2020. Today, Moving Picture, Audio and Data Coding by Artificial Intelligence – MPAI – has been established as an international non-profit organisation in Geneva, Switzerland, at a conference call attended by 33 members from 15 countries.

One driving force behind MPAI is the need to have an organisation responsive to industry needs that devel­ops data cod­ing standards for a range of applications with Artificial Intelligence (AI) as its core enabling techn­ology. In the past, the sheer reduction of the amount of data – i.e. com­pression – has been the success factor for a variety of businesses that range from broad­cas­ting, to telecommunication, information technology and related manufacturing indus­tries.

In response to the demand for more compression MPAI intends to develop AI-enab­led standards that further improve the coding efficiency of data types that have already benefited from com­pression and bring the benefits of coding to new data types. An example of AI-enabled coding is to “bring out” aspects of the data semantics relevant to an application.

MPAI believes that, to ensure the success of its standards in the fast-evolving AI field, it must leverage its connection with academia and industrial research – some 40% of the current MPAI members are academic and res­earch institutions.

Another motivation to create MPAI is to overcome the limitations of the Fair, Reasonable and Non-Discriminatory (FRAND) licensing declarations, a burning issue for many standard devel­oping organisations and their industries. MPAI plans to develop, for each MPAI stan­dard, a “framework licence”, i.e. the business model, without values, dates and percentages, that stan­dard essential patent holders intend to use to monetise their patents eventually adopted in the standard.

MPAI has been moving fast. In the past two nonths, a large group of interested people have col­labor­ated to create a set of use cases, some to become standard projects soon, now that MPAI is established. A project that is quickly taking shape is Context-based Audio Enhancement (MPAI-CAE) to improve the user exper­ience in a variety of contexts of practical interest. Multiuser games, AI-assisted driving, and typ­ical “big data” fields such as financial and genomics are also fast-maturing use cases.

Experts from industry, science and academia are invited to join MPAI and help promote data-driven applications through AI-enabled standards.

About MPAI

MPAI is a non-profit, unaffiliated association whose goal is to develop AI enabled digital data compression specifications with clear IPR licensing frameworks.

Any entity, such as corporation and individual firm, partnership, university, governmental body or international organisation supporting the mission of MPAI may apply for membership, provided that it is able to contribute to the development of technical specifications for the efficient use of data.

Contact

For further information, please see https://mpai.community for openly accessible documents or contact leonardo@chiariglione.org. Information on MPAI-CAE can be found here. The list of use cases being considered can be found here.

A new organisation dedicated to data compression standards based on Artificial Intelligence

2020/09/30

Geneva, 30 September 2020. Today, Moving Picture, Audio and Data Coding by Artificial Intelligence – MPAI – has been established as an international non-profit organisation in Geneva, Switzerland, at a conference call attended by 33 members from 15 countries.

One driving force behind MPAI is the need to have an organisation responsive to industry needs that develops data coding standards for a range of applications with Artificial Intelligence (AI) as its core enabling technology. In the past, the sheer reduction of the amount of data – i.e. com­pression – has been the success factor for a variety of businesses that range from broad­cas­ting, to telecommunication, information technology and related manufacturing indus­tries.

In response to the demand for more compression MPAI intends to develop AI-enab­led standards that further improve the coding efficiency of data types that have already benefited from com­pression and bring the benefits of coding to new data types. An example of AI-enabled coding is to “bring out” aspects of the data semantics relevant to an application.

MPAI believes that, to ensure the success of its standards in the fast-evolving AI field, it must leverage its connection with academia and industrial research – some 40% of the current MPAI members are academic and res­earch institutions.

Another motivation to create MPAI is to overcome the limitations of the Fair, Reasonable and Non-Discriminatory (FRAND) licensing declarations, a burning issue for many standard devel­oping organisations and their industries. MPAI plans to develop, for each MPAI stan­dard, a “framework licence”, i.e. the business model, without values, dates and percentages, that stan­dard essential patent holders intend to use to monetise their patents eventually adopted in the standard.

MPAI has been moving fast. In the past two nonths, a large group of interested people have col­labor­ated to create a set of use cases, some to become standard projects soon, now that MPAI is established. A project that is quickly taking shape is Context-based Audio Enhancement (MPAI-CAE) to improve the user exper­ience in a variety of contexts of practical interest. Multiuser games, AI-assisted driving, and typ­ical “big data” fields such as financial and genomics are also fast-maturing use cases.

Experts from industry, science and academia are invited to join MPAI and help promote data-driven applications through AI-enabled standards.

About MPAI

MPAI is a non-profit, unaffiliated association whose goal is to develop AI enabled digital data compression specifications with clear IPR licensing frameworks.

Any entity, such as corporation and individual firm, partnership, university, governmental body or international organisation supporting the mission of MPAI may apply for membership, provided that it is able to contribute to the development of technical specifications for the efficient use of data.

Contact

For further information, please see https://mpai.community for openly accessible documents or contact secretariat@mpai.community. Information on MPAI-CAE can be found here. The list of use cases being considered can be found here.

MPAI Application Note #1

Context-based Audio Enhancement (MPAI-CAE)

Proponents: Michelangelo Guarise, Andrea Basso (VOLUMIO)

 Description: The overall user experience quality is highly dependent on the context in which audio is used, e.g.

1. Entertainment audio can be consumed in the home, in the car, on public transport, on-the-go (e.g. while doing sports, running, biking) etc.
2. Voice communications: can take place office, car, home, on-the-go etc.
3. Audio and video conferencing can be done in the office, in the car, at home, on-the-go etc.
4. (Serious) gaming can be done in the office, at home, on-the-go etc.
5. Audio (post-)production is typically done in the studio
6. Audio restoration is typically done in the studio

By using context information to act on the content using AI, it is possible substantially to improve the user experience.

Comments: Currently, there are solutions that adapt the conditions in which the user experiences content or service for some of the contexts mentioned above. However, they tend to be vertical in nature, making it dif­ficult to re-use possibly valuable AI-based components of the solutions for differ­ent applications.

MPAI-CAE aims to create a horizontal market of re-usable and possibly context-depending components that expose standard interfaces. The market would become more receptive to innov­ation hence more compet­itive. Industry and consumers alike will benefit from the MPAI-CAE stan­dard.

Examples

The following examples describe how MPAI-CAE can make the difference.

1. Enhanced audio experience in a conference call

Often, the user experience of a video/audio conference can be marginal. Too much background noise or undesired sounds can lead to participants not understanding what participants are saying. By using AI-based adaptive noise-cancellation and sound enhancement, MPAI-CAE can virtually eliminate those kinds of noise without using complex microphone systems to capture environment characteristics.

2. Pleasant and safe music listening while biking

While biking in the middle of city traffic, AI can process the signals from the environment captured by the microphones available in many earphones and earbuds (for active noise cancellation), adapt the sound rendition to the acoustic environment, provide an enhanced audio experience (e.g. performing dynamic signal equalization), improve battery life and selectively recognize and allow relevant environment sounds (i.e. the horn of a car). The user enjoys a satisfactory listening experience without losing contact with the acoustic surroundings.

3. Emotion enhanced synthesized voice

Speech synthesis is constantly improving and finding several applications that are part of our daily life (e.g. intelligent assistants). In addition to improving the ‘natural sounding’ of the voice, MPAI-CAE can implement expressive models of primary emotions such as fear, happiness, sad­ness, and anger.

4. Efficient 3D sound

MPAI-CAE can reduce the number of channels (i.e. MPEG-H 3D Audio can support up to 64 loudspeaker channels and 128 codec core channels) in an automatic (unsupervised) way, e.g. by mapping a 9.1 to a 5.1 or stereo (radio broadcasting or DVD), maintaining the musical touch of the composer.

5. Speech/audio restoration

Audio restoration is often a time-consuming process that requires skilled audio engineers with specific experience in music and recording techniques to go over manually old audio tapes. MPAI-CAE can automatically remove anomalies from recordings through broadband denoising, declicking and decrackling, as well as removing buzzes and hums and performing spectrographic ‘retouching’ for removal of discrete unwanted sounds.

6. Normalization of volume across channels/streams

Eighty-five years after TV has been first introduced as a public service, TV viewers are still strug­gling to adapt to their needs the different average audio levels from different broadcasters and, within a program, to the different audio levels of the different scenes.

MPAI-CAE can learn from user’s reactions via remote control, e.g. to a loud spot, and control the sound level accordingly.

7. Automotive

Audio systems in cars have steadily improved in quality over the years and continue to be integrated into more critical applications. Toda, a buyer takes it for granted that a car has a good automotive sound system. In addition, in a car there is usually at least one and sometimes two microphones to handle the voice-response system and the hands-free cell-phone capability. If the vehicle uses any noise cancellation, several other microphones are involved.

MPAI-CAE can be used to improve the user experience and enable the full quality of current audio systems by reduc­ing the effects of the noisy automotive environment on the signals.

8. Audio mastering

Audio mastering is still considered as an ‘art’ and the prerogative of pro audio engineers. Normal users can upload an example track of their liking (possibly obtained from similar musical content) and MPAI-CAE analyzes it, extracts key features and generate a master track that ‘sounds like’  the example track starting from the non-mastered track.  It is also possible to specify the desired style without an example and the original track will be adjusted accordingly.

Requirements: The following is an initial set of MPAI-CAE functional requirements to be further developed in the next few weeks. When the full set of requirements will be developed, the MPAI General Assembly will decide whether an MPAI-CAE standard should be developed.

1. The standard shall specify the following natural input signals
   1. Microphone signals
   2. Inertial measurement signals (Acceleration, Gyroscope, Compass, …)
   3. Vibration signals
   4. Environmental signals (Proximity, temperature, pressure, light, …)
   5. Environment properties (geometry, reverberation, reflectivity, …)
2. The standard shall specify
   1. User settings (equalization, signal compression/expansion, volume, …)
   2. User profile (auditory profile, hearing aids, …)
3. The standard shall support the retrieval of pre-computed environment models (audio scene, home automation scene, …)
4. The standard shall reference the user authentication standards/methods required by the specific MPAI-CAE context
5. The standard shall specify means to authenticate the components and pipelines of an MPAI-CAE instance
6. The standard shall reference the methods used to encrypt the streams processed by MPAI-CAE and service-related metadata
7. The standard shall specify the adaptation layer of MPAI-CAE streams to delivery protocols of common use (e.g. Bluetooth, Chromecast, DLNA, …)

 Object of standard: Currently, three areas of standardization are identified:

1. Context type interfaces: a first set of input and output signals, with corresponding syntax and semantics, for audio usage contexts considered of sufficient interest (e.g. audiocon­ferencing and audio consumption on-the-go). They have the following features
   1. Input and out signals are context specific, but with a significant degree of commonality across contexts
   2. The operation of the framework is implementation-dependent offering implementors the way to produce the set of output signals that best fit the usage context
2. Processing component interfaces: with the following features
   1. Interfaces of a set of updatable and extensible processing modules (both traditional and AI-based)
   2. Possibility to create processing pipelines and the associated control (including the needed side information) required to manage them
   3. The processing pipeline may be a combination of local and in-cloud processing
3. Delivery protocol interfaces
   1. Interfaces of the processed audio signal to a variety of delivery protocols

Benefits: MPAI-CAE will bring benefits positively affecting

1. Technology providers need not develop full applications to put to good use their technol­ogies. They can concentrate on improving the AI technologies that enhance the user exper­ience. Further, their technologies can find a much broader use in application domains beyond those they are accustomed to deal with.
2. Equipment manufacturers and application vendors can tap from the set of technologies made available according to the MPAI-CAE standard from different competing sources, integrate them and satisfy their specific needs
3. Service providers can deliver complex optimizations and thus superior user experience with minimal time to market as the MPAI-CAE framework enables easy combination of 3rd party components from both a technical and licensing perspective. Their services can deliver a high quality, consistent user audio experience with minimal dependency on the source by selecting the optimal delivery method
4. End users enjoy a competitive market that provides constantly improved user exper­iences and controlled cost of AI-based audio endpoints.

 Bottlenecks: the full potential of AI in MPAI-CAE would be unleashed by a market of AI-friendly processing units and introducing the vast amount of AI technologies into products and services.

 Social aspects: MPAI-CAE would free users from the dependency on the context in which they operate; make the content experience more personal; make the collective service experience less dependent on events affecting the individual participant and raise the level of past content to today’s expectations.

Success criteria: MPAI-CAE should create a competitive market of AI-based components expos­ing standard interfaces, processing units available to manufacturers, a variety of end user devices and trigger the implicit need felt by a user to have the best experience whatever the context.

Geneva, 2020/09/12

Formation of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) was announced in July 2020. It is planned to be established as a non-profit organisation by the end of September 2020. It will develop technical specifications of data coding, especi­ally using Artificial Intelligence, and their integration in Information and Communication Technology systems, brid­ging the gap between its technical specifications and their practical use through Intellectual Property Rights Guidelines, such as Framework Licences.

Today MPAI announces that one use case – Context-based Audio Enhancement (MPAI-CAE) – has reached sufficient maturity to warrant the start of the next stage where detailed functional requirements are identified.

MPAI-CAE addresses a variety of consumer-oriented use cases, e.g. entertain­ment, voice com­munication, audio conferencing, gaming etc. relevant to different contexts – e.g., at home, in the car and on the go – that may greatly influence the audio experience. MPAI-CAE also addresses professional applications such as audio (post-)production and restoration.

The MPAI-CAE standard will specify

1. Input and output interfaces for a set of contexts
2. Interfaces of updatable and extensible processing modules, both traditional and AI-based, to create processing pipelines for possibly partly local and partly on-the-cloud execution
3. Interfaces of the processed audio signals to a variety of delivery protocols.

MPAI envisages that technology providers will benefit from a wider usage of their technologies beyond their specific domains; application vendors adopting the emerging MPAI-CAE standard will be able to tap from the common set of technologies to support their specific needs; service providers will benefit from an accelerated delivery by being able to integrate third parties’ components from both a technical and licensing perspective; and end users will be able to tap from a competitive market providing constantly improved user experiences and AI-based audio endpoints.

MPAI is investigating several other draft projects in the area of coding of still and moving pictures, event sequences and other data such as interferometric data for gravitational-wave detection and genomic data. They are expected to become standard develop­ment projects as they mature.

About MPAI

MPAI is a non-profit, unaffiliated association whose goal is to establish a set of standards for advanced audio, video and data coding using artificial intelligence and to establish procedures that facilitate the timely and effective use of the standards it develops.

Any entity, such as corporation and individual firm, partnership, university, governmental body or international organisation supporting the mission of MPAI may apply for membership, provided that it is able to contribute to the development of technical specifications for the efficient use of data.

For further information, please contact leonardo@chiariglione.org and see https://mpai.community for MPAI and https://mpai.community/2020/09/12/mpai-cae/ for more details on MPAI-CAE.

Geneva, 2020/09/12 – Formation of Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) was announced in July 2020. It is planned to be established as a non-profit organisation by the end of September 2020. It will develop technical specifications of data coding, especi­ally using Artificial Intelligence, and their integration in Information and Communication Technology systems, brid­ging the gap between its technical specifications and their practical use through Intellectual Property Rights Guidelines, such as Framework Licences.

Today MPAI announces that one use case – Context-based Audio Enhancement (MPAI-CAE) – has reached sufficient maturity to warrant the start of the next stage where detailed functional requirements are identified.

MPAI-CAE addresses a variety of consumer-oriented use cases, e.g. entertain­ment, voice com­munication, audio conferencing, gaming etc. relevant to different contexts – e.g., at home, in the car and on the go – that may greatly influence the audio experience. MPAI-CAE also addresses professional applications such as audio (post-)production and restoration.

The MPAI-CAE standard will specify:

1. Input and output interfaces for a set of contexts
2. Interfaces of updatable and extensible processing modules, both traditional and AI-based, to create processing pipelines for possibly partly local and partly on-the-cloud execution
3. Interfaces of the processed audio signals to a variety of delivery protocols.

MPAI envisages that technology providers will benefit from a wider usage of their technologies beyond their specific domains; application vendors adopting the emerging MPAI-CAE standard will be able to tap from the common set of technologies to support their specific needs; service providers will benefit from an accelerated delivery by being able to integrate third parties’ components from both a technical and licensing perspective; and end users will be able to tap from a competitive market providing constantly improved user experiences and AI-based audio endpoints.

MPAI is investigating several other draft projects in the area of coding of still and moving pictures, event sequences and other data such as interferometric data for gravitational-wave detection and genomic data. They are expected to become standard develop­ment projects as they mature.

About MPAI

MPAI is a non-profit, unaffiliated association whose goal is to establish a set of standards for advanced audio, video and data coding using artificial intelligence and to establish procedures that facilitate the timely and effective use of the standards it develops.

Any entity, such as corporation and individual firm, partnership, university, governmental body or international organisation supporting the mission of MPAI may apply for membership, provided that it is able to contribute to the development of technical specifications for the efficient use of data.

For further information, please contact leonardo@chiariglione.org and see https://mpai.community for MPAI and https://mpai.community/2020/09/12/mpai-cae/ for more details on MPAI-CAE.

For 30+ years, digital media have been the powerful driver that has fostered research, industry and commer­ce. The engine that has sustained the development could expand its coverage and provide new standards to a growing group of client industries. Academia and research, all facets of industry, and billions of users have benefited from this bonanza.

Unfortunately, the engine has run out of steam – technology-wise and business-wise.

Thirty years of practical data compression show the importance of the business that is built of data compression standards. Old technology has had its day. To renew it, we need two things: fresh new technologies, but also a fresh new approach to the field.

A new engine is coming to rescue. There is a vast group of technologies – going under the general name of Artificial Intelligence – that provide alternative and more promising approaches than statistical correlation. They go deeper understanding what are the physical phenomena we are trying to represent.

Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI) is the vehicle designed to implement the plan. It is a win-win proposal because Digital Media gets more performing technologies and Artificial Intelligence extends the range where its technologies are applied – not just digital media, but also other data types whose use can be more effective if converted to a more efficient representation.

The MPAI Statutes define data coding as the transformation of data from one representation into another representation that is more convenient for a particular purpose. Reducing the amount of data, a.k.a. compression, is one purpose that has proved to be very important to billions of people, but there are many other purposes. Having AI as the underlying technology layer will ensure that AI technologies for data coding will have wider applications, practical deployment will be accelerated and interoperability improved.

This is the grand plan, but we should not forget that the devil is in the details. MPEG has shown that technically excellent standards are no guarantee that their access will be easy and their use possible. Therefore, MPAI abandons the old FRAND approach because it does not guarantee that a licence for a supposed FRAND standard will be available. It embraces instead the Framework Licence approach where IPR holders agree to a business model, and possibly a cap to the total cost of a licence, _before_ the work on the standard starts.

MPAI attacks the main issue of the digital world – data representation, i.e. coding – and leverages AI to get the best results achievable in the current time frame. However, it has learnt the lesson: industry is no longer available to wait for the terms after the standard is done. They want to know more before starting the work.

MPAI held its 3rd preparatory meeting on 2020/08/24T14:00-16:00 UTC and decided to initiate or continue the following five areas of activity

1. Development of a bibliography of papers and patents on “Video coding by AI”
2. Development of an annotated list of entities – associations and institutes – who have AI in their mission
3. Development of use cases of AI-enabled data compression (so far some 30 use cases have been collected). Each use case is and will be described according to the following format
   1. Proponent: name of the member who proposed the use case
   2. Application description: a detailed description of the use case
   3. Comments: any comment that may clarify the use case
   4. What is standardised: as each use case is a candidate to enter the MPAI work plan, we need to understand what interface, format etc. requires standardisation
   5. Characterization table: a collection of relevant characteristics
      1. Expected benefits
      2. Volume
      3. Quality Criteria
      4. Maturity
      5. Relevance of AI
      6. Users
      7. Players
      8. Level of interest
4. Identification and description, including structure, of all data types that are potentially the target of MPAI standardisation (the compression, not the data)
5. Identification of conditions for MPAI standardisation. Standard for compressing data by AI are likely to be driven by a different logic than done so far in a non-AI context. Three conditions have already been identified.

A series of meeting has been planned (all meetings at 14:00 UTC)

For more details please contact Leonardo

One reason for creating MPAI is to respond to the needs of the MPEG constituency, ill-served by ISO’s self-imposed “FRAND” constraints and by its lack of reaction to the changes of the industry induced by MPEG standards and the effects wrought by industry changes on MPEG itself. MPAI intends to reverse the trend that has made progressively harder, especially for some industries, to use MPEG standards. MPAI does not believe that the alternative of offering “royalty free” standards to industries is sustainable even in the short term.

MPAI takes an antipodal attitude to MPEG with respect to the nature of the requirements that drive the work on a standard. In MPEG functional requirements, made widely known to industry, used to drive the development of a standard. Users were left to “discover” the commercial terms when the standard was done, possibly 4 years after the start of the work, but actually much later than that because of the time it usually took to develop licence(s) and in some cases, never.

MPAI would love to make both functional and commercial requirements available to users. However, providing a full set of commercial requirements may not by supported by antitrust regulations. Therefore, MPAI comes as close as possible to that by making known to users the business model, that MPAI calls Framework Licence (FWL), that IPR holders will eventually apply in their licence(s). The FWL does not contain the monetary values and other data that would be frown upon by antitrust authorities.

These are the main features of the MPAI FWL

1. As a minimum, the FWL will state that the total cost of the license(s) will be in line with the total cost of the licenses for similar data coding/decoding technologies, considering the market value of the specific technology. While this is the minimum, the FWL may go as far as to provide a cap on the total licence cost.
2. The FWL will also state that access to the standard shall be granted in a non-discriminatory fashion.
3. The FWL may envisage that IPR holders make available their patents if all IPR holders agree to do so without requiring a licence. Of course, if certain events specified in the FWL happen, e.g. IPR holders may decide to withdraw their offer. Therefore, the FWL specifies the terms of the licence, but not the values, that IPR holders will make available in case such events happen.
4. Documents submitted by MPAI members that relate to a standard shall contain a declaration that the proponent will make available the terms of the Licence related to their patents according to the FWL, alone or jointly with other IPR holders after the standard is approved and not after commercial implementations of the standard become available on the market.
5. Each member will declare it will take a Licence for the patents held by other members, if used, within one year from the publication by patent holders of their licence terms. Non-members remain obligated to acquire licences to use MPAI standards as mandated by the legislation of the territories in which they use MPAI standards.
6. Each MPAI member shall inform the Secretariat of the result of its best effort and transparent identification of IP that it believes is infringed by a standard that is being or has already been developed by MPAI.
7. Finally, when the MPAI standard is approved, IPR holders express their preference on the entity that should administer the patent pool of the standard.

So far, we have talked about how MPAI intends to work, but that is not the only driver. MPAI intends to work differently also on the content of the standards.

After decades of hardly visible work by researchers, Artificial Intelligence (AI) is arousing the attention of the public at large. Various AI technologies have been and are being investigated with the goal to provide more efficient and more intelligent compression. MPAI retains the proposal made by the Italian Standards Organisation UNI in 2018 to consider coding as a single field of which instances are: images, moving pictures, audio, 3D Graphics and other data such as those generated in manufacturing, automotive, health and other fields, and generic data.

Even though MPAI has not been formally incorporated, experts are busy collecting use cases where AI-enabled coding can provide new solutions that enhance industry performance while benefitting end users.

Fair, reasonable and non-discriminatory (FRAND) is the combination of adjectives that have been commonly used to indicate the way patent holders intended to licence their technologies in standards produced by many standardisation bodies and industry fora.

Decades ago, these adjectives were easily applicable to a standard. When JVC submitted their VHS cassette recording system to IEC for standardisation and IEC produced IEC 60774 – Helical-scan video tape cassette system using 12,65 mm (0,5 in) magnetic tape on type VHS, JVC submitted an FRAND declaration. The companies manufacturing VHS recorders knew exactly whom to call to get a licence. It was a small world with a limited number of players who knew each other since decades.

The same FRAND declaration was used in the MPEG-2 case. But the world at that time (1995) had already changed. As MPEG adopted the policy “any technology that proves its worth and is accompanied by an FRAND declaration”, not only the number of licensors skyrocketed to about 30, but licensors included Consumer Electronic companies, telcos, telco manufacturers, IT companies, some Non-Performing Entities (NPE) and a university.

MPEGLA created an MPEG-2 patent pool that gathered most patent holders and contributed to make MPEG-2 the first big success in a converged world.

Today the situation is again different than 25 years ago. As I have already said a few times, the HEVC standard has almost two times as many the number of patent holders of MPEG-2, three patent pools and several patent holders who did not join any patent pool.

Moving Picture, Audio and Data Coding by Artificial Intelligence (MPAI), is a non-profit organisation to be established soon in Geneva. MPAI has identified the standardisation process based on accepting FRAND declarations as unable to cope with the tectonic changes that have affected the industry during the 30 years since work on the successful MPEG-2 standard started.

MPAI acknowledges the value of unconstrained collaboration adopted by MPEG. This should be retained, however, leaving to the market the task of sorting out all commercial aspects related to a standard may lead to situations of confusion like in HEVC and MPEG-H 3D Audio.

Let’s consider the main steps of the MPEG process

1. Develop the functional requirements of a standard
2. Accept the organisation’s commercial requirements in the form of FRAND declaration
3. Publish a Call for Proposals asking for technologies that satisfy the functional and commercial requirements
4. Develop a standard that satisfies the functional and commercial requirements, i.e. using technologies that proponents have declared they will licence at FRAND terms.

The MPEG process assumed that the market would find a way to remunerate patent holders. This did happen, until patent pools stopped working, as the HEVC case and MPEG 3D Audio case demonstrate. The MPEG-style commercial requirements are no match to the needs of the industry today.

MPAI believes that it should be possible to move the development of some commercial aspects that do not affect the constraints of the competition law to a phase that precedes the technical work. This does not mean that commercial aspects will be mixed with the development of standards.

In other words, MPAI adopts the following modifications of the steps of the MPEG standardisation process (in red the MPAI modifications)

1. Develop the functional requirements of a standard
2. Develop commercial requirements, in the form of  a “Framework Licence” (FWL)
3. Publish a Call for Proposals requesting technologies that satisfy the functional and commercial requirements
4. Develop a standard that satisfies the functional and commercial requirements, i.e. using technologies the proponents have declared they will license in line with the FWL.

The FWL is the business model to remunerate IPRs in the standard that does not bear values: no $, no %, no dates etc. A FWL is obviously standard specific.

Thus, the MPAI policy becomes “any technology provided it proves its worth and is accompanied by a Framework Licence declaration”.

The table compares the two processes:

The MPAI process is a major improvement to the MPEG process because the big problem of simultaneously defining the business model and the price of the patented technologies, is split in two: the business model before work on the standard starts (doable because the functional requirements are known), and the monetary values after the work on the standard is finished (doable because every patent holder at that time can make their assessment of the worth of their technology).

There are advantages for users of the standard as well because they know the business model of the standard before work on the standard even starts. Actually, the FWL can even set a cap to the total cost of the standard.

In a multi-polar world where there are multiple sources of coding standards, users can only be attracted if they know what they are committing to before work on the standard starts. It is no longer possible to promise technical wonders, today, and discover a business disaster, years later.

MPAI offers a process that accelerates the practical use of standardised technologies benefitting industry and end users alike.

What is data

Data can be defined as the digital representation of an entity. The entity can have different attributes be physical, virtual, logical or other.

A river may be represented by its length, its average width, its max, min, average flow, the coordinates of its bed from the source to its mouth and so on. Typically, different data of an entity are captured depending on the intended use of the data. If the use of a river data is for agriculture, the depth of the river, the flow during the seasons, the width at a certain point, the nature of the soil etc. are likely to be important.

Video and audio intended for consumption by humans are data types characterised by a large amount of data, typically samples of the visual and audio information: tens/hundreds/thousands/millions of Mbit/s for video, and tens/hundreds/thousands of kbit/s for audio. If we exclude niche cases, this amount of data is unsuited to storage and transmission.

High-speed sequencing machines produce snapshots of randomly taken segments with unknown coordinates of a DNA sample. As the “reading” process is noisy, the value of each nucleotide is assigned a “quality value” typically expressed by an integer. As a nucleotide must be read several tens of times to achieve a sufficient degree of confidence, the size of whole genome sequencing files may reach TeraBytes. This digital representation of a DNA sample made of unordered and unaligned reads is unsuited to storage and transmission but is also unsuited to extracting vital information for a medical doctor to make a diagnosis.

Data and information

Data become information when their representation makes them suitable to a particular use.

Tens of thousands of researcher-years were invested in studying the problem and finding practical ways conveniently to represent audio and visual data.

For several decades, facsimile compression became the engine that drove efficiency in the office by offering high quality prints at 1.6 of the early analogue facsimile machines.

Reducing the audio data rate by a factor of 10-20, as offered by MP3 and AAC, changed the world of music forever. Reducing the video data rate by a factor of 1,000, as achieved by the latest MPEG-I VVC standard, multiplies the way humans can have visual experience. Surveillance applications developed alternative ways to represent audio and video that allowed, for instance, event detection, object counting etc.

The MPEG-G standard, developed to convert DNA reads into information that needs less bytes to be represented, also gives easier access to information that is of immediate interest to a medical doctor.

These examples of transformation of data from “dull” into “smart” collection of numbers has largely been achieved by using the statistical properties of the data or their transformations. Although quite dated, the method used to compress facsimile information is emblematic. The Group 3 facsimile algorithm defines two statistical variables: the length of “white” or “black” runs, i.e. the number of white/black points following and including the first white/black point after a black point) until a black/white point is encountered. A machine that had been trained to read billions of pages could develop an understanding of how business documents are typically structured and probably be able to use less bits (and probably more meaningful to an application) to represent a page of a business document.

CABAC is another more sophisticated example of data compression using statistical method. CA in CABAC stands for “Context-Adaptive”, i.e. the code set is adapted to the local statistics and B stands for Binary, because all variable are converted to and handled in binary form. CABAC can be applied to statistical variables which do not have uniform statistical characteristics. A machine that had been trained by observing how the statistical variable changes depending on other parameters should probably be able to use less and more meaningful bits to represent the data.

The end of data coding as we know it?

Many approaches at data coding were developed having in mind to exploit some statistical properties in some data transformations. The two examples given above show that Machine Learning can probably be used to provide more efficient solutions to data coding than traditional methods could provide.

It should be clear that there is no reason to stay confined to the variables exploited in the “statistical” phase of data coding. There are probably better ways to use machines’ learning capabilities on data processed by different methods.

The MPAI work plan is not set yet, but one proposal is to see, on the one hand, how far Artificial Intelligence can be applied to the “old” variables and how far fresh approaches can transform data into better usable information.

Smart application of Artificial Intelligence promises to do a better job in converting data into information than statistical approaches have done so far. Delivering on the promise is MPAI’s mission.