Generative Compression

Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed. Here we describe the concept of generative compression, the compression of data using generative models, and suggest that it is a direction worth pursuing to produce more accurate and visually pleasing reconstructions at much deeper compression levels for both image and video data. We also demonstrate that generative compression is orders-of-magnitude more resilient to bit error rates (e.g. from noisy wireless channels) than traditional variable-length coding schemes

HyperThumbnail: Real-time 6K Image Rescaling with Rate-distortion Optimization

Contemporary image rescaling aims at embedding a high-resolution (HR) image into a low-resolution (LR) thumbnail image that contains embedded information for HR image reconstruction. Unlike traditional image super-resolution, this enables high-fidelity HR image restoration faithful to the original one, given the embedded information in the LR thumbnail. However, state-of-the-art image rescaling methods do not optimize the LR image file size for efficient sharing and fall short of real-time performance for ultra-high-resolution (e.g., 6K) image reconstruction. To address these two challenges, we propose a novel framework (HyperThumbnail) for real-time 6K rate-distortion-aware image rescaling. Our framework first embeds an HR image into a JPEG LR thumbnail by an encoder with our proposed quantization prediction module, which minimizes the file size of the embedding LR JPEG thumbnail while maximizing HR reconstruction quality. Then, an efficient frequency-aware decoder reconstructs a high-fidelity HR image from the LR one in real time. Extensive experiments demonstrate that our framework outperforms previous image rescaling baselines in rate-distortion performance and can perform 6K image reconstruction in real time.Comment: Accepted by CVPR 2023; Github Repository: https://github.com/AbnerVictor/HyperThumbnai

Properties of Galaxies Hosting X-ray Selected Active Galactic Nuclei in the Cl1604 Supercluster at z=0.9

To investigate the role of feedback from Active Galactic Nuclei (AGN) in driving the evolution of their host galaxies, we have carried out a study of the environments and optical properties of galaxies harboring X-ray luminous AGN in the Cl1604 supercluster at z~0.9. Making use of Chandra, HST/ACS and Keck/DEIMOS observations, we examine the integrated colors, morphologies and spectral properties of nine moderate-luminosity (L_x ~ 10^43 erg s^-1) type 2 Seyferts detected in the Cl1604 complex. We find that the AGN are predominantly hosted by luminous spheroids and/or bulge dominated galaxies which have colors that place them in the valley between the blue cloud and red sequence in color-magnitude space, consistent with predictions that AGN hosts should constitute a transition population. Half of the hosts have bluer overall colors as a result of blue resolved cores in otherwise red spheroids and a majority show signs of recent or pending interactions. We also find a substantial number exhibit strong Balmer absorption features indicative of post-starburst galaxies, despite the fact that we detect narrow [OII] emission lines in all of the host spectra. If the [OII] lines are due in part to AGN emission, as we suspect, then this result implies that a significant fraction of these galaxies (44%) have experienced an enhanced level of star formation within the last ~1 Gyr which was rapidly suppressed. Overall we find that the properties of the nine host galaxies are generally consistent with a scenario in which recent interactions have triggered both increased levels of nuclear activity and an enhancement of centrally concentrated star formation, followed by a rapid truncation of the latter, possibly as a result of feedback from the AGN itself. [Abridged]Comment: 15 pages, 9 Figures, submitted to Ap

The Archigram Archive

The Archigram archival project made the works of seminal experimental architectural group Archigram available free online for an academic and general audience. It was a major archival work, and a new kind of digital academic archive, displaying material held in different places around the world and variously owned. It was aimed at a wide online design community, discovering it through Google or social media, as well as a traditional academic audience. It has been widely acclaimed in both fields. The project has three distinct but interlinked aims: firstly to assess, catalogue and present the vast range of Archigram's prolific work, of which only a small portion was previously available; secondly to provide reflective academic material on Archigram and on the wider picture of their work presented; thirdly to develop a new type of non-ownership online archive, suitable for both academic research at the highest level and for casual public browsing. The project hybridised several existing methodologies. It combined practical archival and editorial methods for the recovery, presentation and contextualisation of Archigram's work, with digital web design and with the provision of reflective academic and scholarly material. It was designed by the EXP Research Group in the Department of Architecture in collaboration with Archigram and their heirs and with the Centre for Parallel Computing, School of Electronics and Computer Science, also at the University of Westminster. It was rated 'outstanding' in the AHRC's own final report and was shortlisted for the RIBA research awards in 2010. It received 40,000 users and more than 250,000 page views in its first two weeks live, taking the site into twitter’s Top 1000 sites, and a steady flow of visitors thereafter. Further statistics are included in the accompanying portfolio. This output will also be returned to by Murray Fraser for UCL

ISAAC/VLT observations of a lensed galaxy at z=10.0

We report the first likely spectroscopic confirmation of a z 10.0 galaxy from our ongoing search for distant galaxies with ISAAC/VLT. Galaxy candidates at z >~ 7 are selected from ultra-deep JHKs images in the core of gravitational lensing clusters for which deep optical imaging is also available, including HST data. The object reported here, found behind Abell 1835, exhibits a faint emission line detected in the J band, leading to z=10.0 when identified as Ly-a, in excellent agreement with the photometric redshift determination. Redshifts z < 7 are very unlikely for various reasons we discuss. The object is located on the critical lines corresponding to z=9 to 11. The magnification factor \mu ranges from 25 to 100. For this object we estimate SFR(Ly-a) (0.8-2.2) Msun/yr and SFR(UV) (47-75) Msun/yr, both uncorrected for lensing. The steep UV slope indicates a young object with negligible dust extinction. SED fits with young low-metallicity stellar population models yield (adopting mu=25) a lensing corrected stellar mass of M*~8.e+6 Msun, and luminosities of 2.e+10 Lsun, corresponding to a dark matter halo of a mass of typically M_tot>~ 5.e+8 Msun. In general our observations show that under excellent conditions and using strong gravitational lensing direct observations of galaxies close to the ``dark ages'' are feasible with ground-based 8-10m class telescopes.Comment: To be published in A&A, Vol. 416, p. L35. Press release information, additional figures and information available at http://obswww.unige.ch/sfr and http://webast.ast.obs-mip.fr/galaxie

Adaptive Nonparametric Image Parsing

In this paper, we present an adaptive nonparametric solution to the image parsing task, namely annotating each image pixel with its corresponding category label. For a given test image, first, a locality-aware retrieval set is extracted from the training data based on super-pixel matching similarities, which are augmented with feature extraction for better differentiation of local super-pixels. Then, the category of each super-pixel is initialized by the majority vote of the $k$-nearest-neighbor super-pixels in the retrieval set. Instead of fixing $k$ as in traditional non-parametric approaches, here we propose a novel adaptive nonparametric approach which determines the sample-specific k for each test image. In particular, $k$ is adaptively set to be the number of the fewest nearest super-pixels which the images in the retrieval set can use to get the best category prediction. Finally, the initial super-pixel labels are further refined by contextual smoothing. Extensive experiments on challenging datasets demonstrate the superiority of the new solution over other state-of-the-art nonparametric solutions.Comment: 11 page