Skip to main content

A Comprehensive Framework For 2D-Jnd Extension To 360-Deg Images

Sami Jaballah, Chaker Larabi, Amegh Bhavsar

  • SPS
    Members: Free
    IEEE Members: $11.00
    Non-members: $15.00
    Length: 14:56
04 May 2020

Masking effect is one of the most important perceptual properties that could be modeled by estimating an adaptive threshold known as the just noticeable difference (JND) referring to the maximum difference not perceived by the human visual system (HVS). In this paper, a novel framework to extend 2D-JND models to estimate thresholds for 360-degree images is proposed. The JND is estimated by viewports instead of applying it to the projected format image. Then, the viewport-based JND maps are back-projected to obtain the 360-JND map. To reduce the visible boundaries between viewports an alpha blending process is applied. The validation of the proposed framework is made using subjective experiments. It demonstrates that when applied to 360-degree images, the proposed framework outperforms the 2D-JND models in terms of observers preference at the same noise level.

Value-Added Bundle(s) Including this Product

More Like This

  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00
  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00
  • SPS
    Members: $150.00
    IEEE Members: $250.00
    Non-members: $350.00