Picture quality diagnostics

• US 20030174212A1
• Filed: 03/18/2002
• Published: 09/18/2003
• Est. Priority Date: 03/18/2002
• Status: Active Grant

• Current Assignee: Project Giants, LLC
• Original Assignee: Tektronix Incorporated (Fortive Corp.)
• Inventors: Ferguson, Kevin M.
• Granted Patent: US 7,102,667 B2
• Time in Patent Office: Days
• US Class Current: 348/181
• CPC Class Codes: H04N 17/00 Diagnosis, testing or measuring for television systems or their details

Abstract

A picture quality diagnostics apparatus and method generates a human vision model response based on a human vision model for a test input video signal. Also objective measure maps for different impairment types are generated from the test input video signal. The objective measure maps are applied as masks to the human vision model response to produce objectively filtered subjective maps. The objectively filtered subjective maps are analyzed to give the respective proportions of the different objective impairment types contributing to perceptual impairment or difference for the test input video signal.

Claims

1. A picture quality diagnostic apparatus comprising:

means for generating a human vision model response for a test input video signal based on a human vision model;

means for applying to the human vision model response an objective measures map based on the test input video signal for different impairment types as a mask to produce an objectively filtered subjective map; and

means for determining from the objectively filtered subjective mask an impairment analysis to give respective proportions of the different impairment types.

2. The apparatus as recited in claim 1 wherein the generating means comprises:

means for generating a perceptual difference map for the test input video based on a human vision perceptual model;

means for generating an attention importance map for the test input video based on a human attention model and an input from the perceptual difference map generating means;

means for multiplying the perceptual difference map with the attention importance map to produce a first subjective map; and

means for selecting between the perceptual difference map, the attention importance map and the first subjective map as the human vision model response.

3. The apparatus as recited in claims 1 or 2 wherein the applying means comprises:

means for obtaining an objective measures map for the test input video signal based on an objective measures algorithm for each of the different impairment types;

means for multiplying the objective measures map with the human vision model response to produce a second subjective map; and

means for selecting between the objective measures map, the human vision model response and the second subjective map as the objectively filtered subjective map.

4. A method of picture quality diagnostics comprising the steps of:

generating a human vision model response for a test input video signal based on a human vision model;

applying to the human vision model response an objective measures map based on the test input video signal for different impairment types as a mask to produce an objectively filtered subjective map; and

determining from the objectively filtered subjective mask an impairment analysis to give respective proportions of the different impairment types.

5. The apparatus as recited in claim 4 wherein the generating step comprises the steps of:

generating a perceptual difference map for the test input video based on a human vision perceptual model;

generating an attention importance map for the test input video based on a human attention model and an input from the perceptual difference map generating means;

multiplying the perceptual difference map with the attention importance map to produce a first subjective map; and

selecting between the perceptual difference map, the attention importance map and the first subjective map as the human vision model response.

6. The apparatus as recited in claims 4 or 5 wherein the applying step comprises the steps of:

obtaining an objective measures map for the test input video signal based on an objective measures algorithm for each of the different impairment types;

multiplying the objective measures map with the human vision model response to produce a second subjective map; and

selecting between the objective measures map, the human vision model response and the second subjective map as the objectively filtered subjective map.