Thomas Richter’s research while affiliated with Fraunhofer Institute for Integrated Circuits and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (168)


Fig. 1. Source HDR images used in this study. All images have a resolution of 840 × 944 pixels. The previews shown here are tone-mapped to SDR and a standard color gamut. To view the HDR source images with accurate color, please visit https://cloudinary.com/labs/aic-3-and-hdr.
Fine-Grained HDR Image Quality Assessment From Noticeably Distorted to Very High Fidelity
  • Preprint
  • File available

June 2025

·

18 Reads

·

Jon Sneyers

·

Davi Lazzarotto

·

[...]

·

High dynamic range (HDR) and wide color gamut (WCG) technologies significantly improve color reproduction compared to standard dynamic range (SDR) and standard color gamuts, resulting in more accurate, richer, and more immersive images. However, HDR increases data demands, posing challenges for bandwidth efficiency and compression techniques. Advances in compression and display technologies require more precise image quality assessment, particularly in the high-fidelity range where perceptual differences are subtle. To address this gap, we introduce AIC-HDR2025, the first such HDR dataset, comprising 100 test images generated from five HDR sources, each compressed using four codecs at five compression levels. It covers the high-fidelity range, from visible distortions to compression levels below the visually lossless threshold. A subjective study was conducted using the JPEG AIC-3 test methodology, combining plain and boosted triplet comparisons. In total, 34,560 ratings were collected from 151 participants across four fully controlled labs. The results confirm that AIC-3 enables precise HDR quality estimation, with 95\% confidence intervals averaging a width of 0.27 at 1 JND. In addition, several recently proposed objective metrics were evaluated based on their correlation with subjective ratings. The dataset is publicly available.

Download




A Study on the Effect of Color Spaces in Learned Image Compression

June 2024

·

12 Reads

In this work, we present a comparison between color spaces namely YUV, LAB, RGB and their effect on learned image compression. For this we use the structure and color based learned image codec (SLIC) from our prior work, which consists of two branches - one for the luminance component (Y or L) and another for chrominance components (UV or AB). However, for the RGB variant we input all 3 channels in a single branch, similar to most learned image codecs operating in RGB. The models are trained for multiple bitrate configurations in each color space. We report the findings from our experiments by evaluating them on various datasets and compare the results to state-of-the-art image codecs. The YUV model performs better than the LAB variant in terms of MS-SSIM with a Bj{\o}ntegaard delta bitrate (BD-BR) gain of 7.5\% using VTM intra-coding mode as the baseline. Whereas the LAB variant has a better performance than YUV model in terms of CIEDE2000 having a BD-BR gain of 8\%. Overall, the RGB variant of SLIC achieves the best performance with a BD-BR gain of 13.14\% in terms of MS-SSIM and a gain of 17.96\% in CIEDE2000 at the cost of a higher model complexity.






Color Learning for Image Compression

June 2023

·

38 Reads

Deep learning based image compression has gained a lot of momentum in recent times. To enable a method that is suitable for image compression and subsequently extended to video compression, we propose a novel deep learning model architecture, where the task of image compression is divided into two sub-tasks, learning structural information from luminance channel and color from chrominance channels. The model has two separate branches to process the luminance and chrominance components. The color difference metric CIEDE2000 is employed in the loss function to optimize the model for color fidelity. We demonstrate the benefits of our approach and compare the performance to other codecs. Additionally, the visualization and analysis of latent channel impulse response is performed.


Citations (55)


... Recent advances in deep learning have led to growing interest in data-driven color space alignment solutions [45]. Environmental and equipment-induced light attenuation varies non-uniformly across channels, leading to inconsistent color stylization [46]. ...

Reference:

AECA-FBMamba: A Framework with Adaptive Environment Channel Alignment and Mamba Bridging Semantics and Details
A Study on the Effect of Color Spaces in Learned Image Compression
  • Citing Conference Paper
  • October 2024

... However, properties of the human visual system are not well exploited. Although the use of YUV color space in image compression is not new, in this work, we build on our prior works [5,6] to shed some light on the effect of color spaces in learned image compression. The performance is compared with state-of-the-art image codecs by means of rate-distortion curves, Bjøntegaard delta bitrate [7], and distortion values. ...

SLIC: A Learned Image Codec Using Structure and Color
  • Citing Conference Paper
  • March 2024

... However, properties of the human visual system are not well exploited. Although the use of YUV color space in image compression is not new, in this work, we build on our prior works [5,6] to shed some light on the effect of color spaces in learned image compression. The performance is compared with state-of-the-art image codecs by means of rate-distortion curves, Bjøntegaard delta bitrate [7], and distortion values. ...

Color Learning for Image Compression
  • Citing Conference Paper
  • October 2023

... Encoding images with DNN models has been rapidly developed in recent years [31,34,51,52]. Despite that several works have encoded CFA images [6,10,12,27,47], there is still a long way to using neural codecs for CFA images on mobile devices with insufficient computing power. Traditional DNN methods targeting to improve humanview-experience by using more parameters in the DNN codec which induces huge time overhead and performance consumption, making ...

Bayer CFA pattern compression with JPEG XS

IEEE Transactions on Image Processing

... Such an implementation was studied in [26], and an end-to-end latency between 70 and 350 lines, depending on the configuration, was reported. The latency also depends on the transport mechanism and whether all packets need to be transported sequentially, or if out-of-order transport is permissible. ...

Parallelization and multi-threaded latency constrained parallel coding of JPEG XS
  • Citing Conference Paper
  • September 2019

... In 2016, the Joint Photographic Experts Group (JPEG), jointly established by the International Organization for Standardization and the International Telegraph and Telephone Advisory Committee (CCITT) under the International Telecommunication Union (ITU), launched a project for low-complexity, low-latency image compression codecs [1], the JPEG XS international standard, to support fields requiring low latency and high quality, such as autonomous driving, virtual reality (VR), and broadcast television. JPEG XS is the latest international standard in the shallow compression domain, called ISO/IEC 21122: JPEG XS lowlatency lightweight image coding system [2,3], the coding standard was officially released in 2019. ...

JPEG-XS-a high-quality mezzanine image codec for video over IP
  • Citing Article
  • October 2018

SMPTE Motion Imaging Journal

... Entropy coding [1,2] is a type of lossless coding to compress digital data by representing frequently occurring patterns with few bits and rarely occurring patterns with many bits. A number of well-known entropy encodings include Huffman coding [3], arithmetic coding and asymmetric numeral systems (ANS) [4]. ...

Entropy Coding and Entropy Coding Improvements of JPEG XS

... Workshop 3D-ARCH "3D Virtual Reconstruction and Visualization of Complex Architectures", 21-23 February 2024, Siena, Italy Unfortunately, the human retina does not have the same unique and objective response to light as the film does. On the other hand, human visual receptors can record a brightness change greater than 10-12 log units, while camera sensors only 5-6 log units (Artusi et al. 2017). Digital cameras are incapable of fully capturing what humans sees with their eyes. ...

High Dynamic Range Imaging Technology.
  • Citing Article
  • September 2017