Search Results for author: Hassene Tmar

Found 5 papers, 1 papers with code

Joint Quality Assessment and Example-Guided Image Processing by Disentangling Picture Appearance from Content

no code implementations20 Apr 2024 Abhinau K. Venkataramanan, Cosmin Stejerean, Ioannis Katsavounidis, Hassene Tmar, Alan C. Bovik

The deep learning revolution has strongly impacted low-level image processing tasks such as style/domain transfer, enhancement/restoration, and visual quality assessments.

Representation Learning Tone Mapping

Cut-FUNQUE: An Objective Quality Model for Compressed Tone-Mapped High Dynamic Range Videos

no code implementations20 Apr 2024 Abhinau K. Venkataramanan, Cosmin Stejerean, Ioannis Katsavounidis, Hassene Tmar, Alan C. Bovik

High Dynamic Range (HDR) videos have enjoyed a surge in popularity in recent years due to their ability to represent a wider range of contrast and color than Standard Dynamic Range (SDR) videos.

Tone Mapping

Encoding Time and Energy Model for SVT-AV1 based on Video Complexity

no code implementations29 Jan 2024 Lena Eichermüller, Gaurang Chaudhari, Ioannis Katsavounidis, Zhijun Lei, Hassene Tmar, Christian Herglotz, André Kaup

We propose an encoding time and energy model for SVT-AV1 based on empirical relations between the encoding time and video parameters as well as encoder configurations.

Bitrate Ladder Construction using Visual Information Fidelity

no code implementations12 Dec 2023 Krishna Srikar Durbha, Hassene Tmar, Cosmin Stejerean, Ioannis Katsavounidis, Alan C. Bovik

Over the past few years, a variety of methods have been proposed to construct optimal bitrate ladders including using low-level features to predict cross-over bitrates, optimal resolutions for each bitrate, predicting visual quality, etc.

Encoder Complexity Control in SVT-AV1 by Speed-Adaptive Preset Switching

1 code implementation11 Jul 2023 Lena Eichermüller, Gaurang Chaudhari, Ioannis Katsavounidis, Zhijun Lei, Hassene Tmar, André Kaup, Christian Herglotz

Current developments in video encoding technology lead to continuously improving compression performance but at the expense of increasingly higher computational demands.

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