Lost in light field compression: Understanding the unseen pitfalls in computer vision

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00382677" target="_blank" >RIV/68407700:21230/25:00382677 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.image.2025.117304" target="_blank" >https://doi.org/10.1016/j.image.2025.117304</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.image.2025.117304" target="_blank" >10.1016/j.image.2025.117304</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Lost in light field compression: Understanding the unseen pitfalls in computer vision

Popis výsledku v původním jazyce

Could we be overlooking a fundamental aspect of light fields in our quest for efficient compression? The vast amount of data enclosed in a light field makes compression a necessity. Yet, from an application point of view, the focus is predominantly on visual consumption while light fields have properties that can potentially be used in various other tasks. This paper examines the impact of light field compression on the performance of subsequent computer vision tasks. We investigate the variations in quality across perspectives and their impact on face recognition systems and disparity estimation. By leveraging a diverse dataset of light field images, we thoroughly evaluate the performance of various face recognition algorithms when subjected to different conventional and learning-based compression techniques, such as JPEG Pleno, ALVC, and SADNQVRF. Our findings reveal a noticeable decline in peak recognition performance as compression levels increase, given specific recognition frameworks. Furthermore, we identify a significant shift in the recognition threshold, particularly in response to higher degrees of compression. Secondly, by relying on a novel disparity estimation algorithm, we explore the loss of information across light field perspectives. Our results highlight a disconnect between the preservation of visual fidelity and the loss of minute detail crucial for the preservation of disparity information in light field images. The findings presented herein aim to contribute to the development of efficient compression strategies while emphasizing the delicate balance between compression efficiency, subjective quality, and feature preservation with the aim of increased accuracy in specialized light field systems.

Název v anglickém jazyce

Lost in light field compression: Understanding the unseen pitfalls in computer vision

Popis výsledku anglicky

Could we be overlooking a fundamental aspect of light fields in our quest for efficient compression? The vast amount of data enclosed in a light field makes compression a necessity. Yet, from an application point of view, the focus is predominantly on visual consumption while light fields have properties that can potentially be used in various other tasks. This paper examines the impact of light field compression on the performance of subsequent computer vision tasks. We investigate the variations in quality across perspectives and their impact on face recognition systems and disparity estimation. By leveraging a diverse dataset of light field images, we thoroughly evaluate the performance of various face recognition algorithms when subjected to different conventional and learning-based compression techniques, such as JPEG Pleno, ALVC, and SADNQVRF. Our findings reveal a noticeable decline in peak recognition performance as compression levels increase, given specific recognition frameworks. Furthermore, we identify a significant shift in the recognition threshold, particularly in response to higher degrees of compression. Secondly, by relying on a novel disparity estimation algorithm, we explore the loss of information across light field perspectives. Our results highlight a disconnect between the preservation of visual fidelity and the loss of minute detail crucial for the preservation of disparity information in light field images. The findings presented herein aim to contribute to the development of efficient compression strategies while emphasizing the delicate balance between compression efficiency, subjective quality, and feature preservation with the aim of increased accuracy in specialized light field systems.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LTT20004" target="_blank" >LTT20004: Spolupráce s mezinárodním výzkumným centrem v oblasti digitálních komunikačních systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Signal Processing: Image Communication

ISSN

0923-5965

e-ISSN

1879-2677

Svazek periodika

136

Číslo periodika v rámci svazku

August

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

1-17

Kód UT WoS článku

001448098900001

EID výsledku v databázi Scopus

2-s2.0-86000597212