Romani et al., 2015 - Google Patents

Full-Reference SSIM Metric for Video Quality Assessment with Saliency-Based Features

Romani et al., 2015

View PDF
Document ID
3387766660704232910
Author
Romani E
da Silva W
Fonseca K
Culibrk D
de Almeida Prado Pohl A
Publication year
Publication venue
International Conference on Image Analysis and Processing

External Links

Snippet

This paper uses models of visual attention in order to estimate the human visual perception and thus improve metrics of Video Quality Assessment. This work reports on the use of the saliency based model in a full-reference structural similarity metric for creating new metrics …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001—Image restoration
    • G06T5/002—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion

Similar Documents

Publication Publication Date Title
Tu et al. Bband index: A no-reference banding artifact predictor
Kim et al. Deep learning of human visual sensitivity in image quality assessment framework
Gu et al. Hybrid no-reference quality metric for singly and multiply distorted images
Appina et al. No-reference stereoscopic image quality assessment using natural scene statistics
CN102271254B (en) A Preprocessing Method of Depth Image
Liang et al. No-reference perceptual image quality metric using gradient profiles for JPEG2000
CN108122206A (en) A kind of low-light (level) image denoising method and device
CN110796615A (en) Image denoising method and device and storage medium
CN115131229A (en) Image noise reduction and filtering data processing method and device and computer equipment
Jakhetiya et al. Perceptually unimportant information reduction and cosine similarity-based quality assessment of 3D-synthesized images
Yan et al. No reference quality assessment for 3D synthesized views by local structure variation and global naturalness change
Gu et al. Structural similarity weighting for image quality assessment
Sonawane et al. Image quality assessment techniques: An overview
Bong et al. An efficient and training-free blind image blur assessment in the spatial domain
CN106485713B (en) Video foreground detection method
Tsai et al. Foveation-based image quality assessment
Romani et al. Full-Reference SSIM Metric for Video Quality Assessment with Saliency-Based Features
Haouassi et al. An efficient image haze removal algorithm based on new accurate depth and light estimation algorithm
Moorthy et al. Image and video quality assessment: Perception, psychophysical models, and algorithms
Chen et al. A universal reference-free blurriness measure
Zhang et al. Local binary pattern statistics feature for reduced reference image quality assessment
Sun et al. No-reference image quality assessment through sift intensity
Li et al. Research on Image Subject Accessing Model Under Foggy Weather
Malik et al. A Unified Dehazing Framework: Synergizing CLAHE and Dark Channel Prior in YCbCr Space to Optimize Quality and Latency
Wiratama et al. Adaptive Gaussian low-pass pre-filtering for perceptual video coding