High-resolution Image Sensors Research Articles

Freeform metasurface color router for deep submicron pixel image sensors.

Advances in imaging technologies have led to a high demand for ultracompact, high-resolution image sensors. However, color filter-based image sensors, now miniaturized to deep submicron pixel sizes, face challenges such as low signal-to-noise ratio due to fewer photons per pixel and inherent efficiency limitations from color filter arrays. Here, we demonstrate a freeform metasurface color router that achieves ultracompact pixel sizes while overcoming the efficiency limitations of conventional architectures by splitting and focusing visible light instead of filtering. This development is enabled by a fully differentiable topology optimization framework to maximize the use of the design space while ensuring fabrication feasibility and robustness to fabrication errors. The metasurface can distribute an average of 85% of incident visible light according to the Bayer pattern with a pixel size of 0.6 μm. The device and design methodology enable the compact, high-sensitivity, and high-resolution image sensors for various modern technologies and pave the way for the advanced photonic device design.

High-Density Polyethylene Custom Focusing Lenses for High-Resolution Transient Terahertz Biomedical Imaging Sensors.

Transient terahertz time-domain spectroscopy (THz-TDS) imaging has emerged as a novel non-ionizing and noninvasive biomedical imaging modality, designed for the detection and characterization of a variety of tissue malignancies due to their high signal-to-noise ratio and submillimeter resolution. We report our design of a pair of aspheric focusing lenses using a commercially available lens-design software that resulted in about 200 × 200-μm2 focal spot size corresponding to the 1-THz frequency. The lenses are made of high-density polyethylene (HDPE) obtained using a lathe fabrication and are integrated into a THz-TDS system that includes low-temperature GaAs photoconductive antennae as both a THz emitter and detector. The system is used to generate high-resolution, two-dimensional (2D) images of formalin-fixed, paraffin-embedded murine pancreas tissue blocks. The performance of these focusing lenses is compared to the older system based on a pair of short-focal-length, hemispherical polytetrafluoroethylene (TeflonTM) lenses and is characterized using THz-domain measurements, resulting in 2D maps of the tissue refractive index and absorption coefficient as imaging markers. For a quantitative evaluation of the lens effect on the image resolution, we formulated a lateral resolution parameter, R2080, defined as the distance required for a 20-80% transition of the imaging marker from the bare paraffin region to the tissue region in the same image frame. The R2080 parameter clearly demonstrates the advantage of the HDPE lenses over TeflonTM lenses. The lens-design approach presented here can be successfully implemented in other THz-TDS setups with known THz emitter and detector specifications.

Synthetic Aperture Radar Radio Frequency Interference Suppression Method Based on Fusing Segmentation and Inpainting Networks

Synthetic Aperture Radar (SAR) is a high-resolution imaging sensor commonly mounted on platforms such as airplanes and satellites for widespread use. In complex electromagnetic environments, radio frequency interference (RFI) severely degrades the quality of SAR images due to its widely varying bandwidth and numerous unknown emission sources. Although traditional deep learning-based methods have achieved remarkable results by directly processing SAR images as visual ones, there is still considerable room for improvement in their performance due to the wide coverage and high intensity of RFI. To address these issues, this paper proposes the fusion of segmentation and inpainting networks (FuSINet) to suppress SAR RFI in the time-frequency domain. Firstly, to weaken the dominance of RFI in SAR images caused by high-intensity interference, a simple CCN-based network is employed to learn and segment the RFI. This results in the removal of most of the original interference, leaving blanks that allow the targets to regain dominance in the overall image. Secondly, considering the wide coverage characteristic of RFI, a U-former network with global information capture capabilities is utilized to learn the content covered by the interference and fill in the blanks created by the segmentation network. Compared to the traditional Transformer, this paper enhances its global information capture capabilities through shift-windows and down-sampling layers. Finally, the segmentation and inpainting networks are fused together through a weighted parameter for joint training. This not only accelerates the learning speed but also enables better coordination between the two networks, leading to improved RFI suppression performance. Extensive experimental results demonstrate the substantial performance enhancement of the proposed FuSINet. Compared to the PISNet+, the proposed attention mechanism achieves a 2.49 dB improvement in peak signal-to-noise ratio (PSNR). Furthermore, compared to Uformer, the FuSINet achieves an additional 4.16 dB improvement in PSNR.

Enhancing phenotyping efficiency in faba bean breeding: integrating UAV imaging and machine learning

Unmanned aerial vehicles (UAVs) equipped with high-resolution imaging sensors have shown great potential for plant phenotyping in agricultural research. This study aimed to explore the potential of UAV-derived red–green–blue (RGB) and multispectral imaging data for estimating classical phenotyping measures such as plant height and predicting yield and chlorophyll content (indicated by SPAD values) in a field trial of 38 faba bean (Vicia faba L.) cultivars grown at four replicates in south-eastern Norway. To predict yield and SPAD values, Support Vector Regression (SVR) and Random Forest (RF) models were utilized. Two feature selection methods, namely the Pearson correlation coefficient (PCC) and sequential forward feature selection (SFS), were applied to identify the most relevant features for prediction. The models incorporated various combinations of multispectral bands, indices, and UAV-based plant height values at four different faba bean development stages. The correlation between manual and UAV-based plant height measurements revealed a strong agreement with a correlation coefficient (R2) of 0.97. The best prediction of SPAD value was achieved at BBCH 50 (flower bud present) with an R2 of 0.38 and RMSE of 1.14. For yield prediction, BBCH 60 (first flower open) was identified as the optimal stage, using spectral indices yielding an R2 of 0.83 and RMSE of 0.53 tons/ha. This development stage presents an opportunity to implement targeted management practices to enhance yield. The integration of UAVs equipped with RGB and multispectral cameras, along with machine learning algorithms, proved to be an accurate approach for estimating agronomically important traits in faba bean. This methodology offers a practical solution for rapid and efficient high-throughput phenotyping in faba bean breeding programs.

Characterization of Partial Discharges in Dielectric Oils Using High-Resolution CMOS Image Sensor and Convolutional Neural Networks.

In this work, an exhaustive analysis of the partial discharges that originate in the bubbles present in dielectric mineral oils is carried out. To achieve this, a low-cost, high-resolution CMOS image sensor is used. Partial discharge measurements using that image sensor are validated by a standard electrical detection system that uses a discharge capacitor. In order to accurately identify the images corresponding to partial discharges, a convolutional neural network is trained using a large set of images captured by the image sensor. An image classification model is also developed using deep learning with a convolutional network based on a TensorFlow and Keras model. The classification results of the experiments show that the accuracy achieved by our model is around 95% on the validation set and 82% on the test set. As a result of this work, a non-destructive diagnosis method has been developed that is based on the use of an image sensor and the design of a convolutional neural network. This approach allows us to obtain information about the state of mineral oils before breakdown occurs, providing a valuable tool for the evaluation and maintenance of these dielectric oils.

In-Situ Classification of Highly Deformed Corrugated Board Using Convolution Neural Networks.

The extensive use of corrugated board in the packaging industry is attributed to its excellent cushioning, mechanical properties, and environmental benefits like recyclability and biodegradability. The integrity of corrugated board depends on various factors, including its geometric design, paper quality, the number of layers, and environmental conditions such as humidity and temperature. This study introduces an innovative application of convolutional neural networks (CNNs) for analyzing and classifying images of corrugated boards, particularly those with deformations. For this purpose, a special device with advanced imaging capabilities, including a high-resolution camera and image sensor, was developed and used to acquire detailed cross-section images of the corrugated boards. The samples of seven types of corrugated board were studied. The proposed approach involves optimizing CNNs to enhance their classification performance. Despite challenges posed by deformed samples, the methodology demonstrates high accuracy in most cases, though a few samples posed recognition difficulties. The findings of this research are significant for the packaging industry, offering a sophisticated method for quality control and defect detection in corrugated board production. The best classification accuracy obtained achieved more than 99%. This could lead to improved product quality and reduced waste. Additionally, this study paves the way for future research on applying machine learning for material quality assessment, which could have broader implications beyond the packaging sector.

Design and Implementation of a Hybrid Optical Camera Communication System for Indoor Applications.

Optical wireless communication is a promising emerging technology that addresses the limitations of radio-frequency-based wireless technologies. This study presents a new hybrid modulation method for optical camera communication (OCC), which integrates two waveforms transmitted from a single transmitter light-emitting diode (LED) and receives data through two rolling shutter camera devices on the receiver side. Then, a smart camera with a high-resolution image sensor captures the high-frequency signal, and a low-resolution image sensor from a smartphone camera captures the low-frequency signal. Based on this hybrid scheme, two data streams are transmitted from a single LED, which reduces the cost of the indoor OCC device compared with transmitting two signals from two different LEDs. In the proposed scheme, rolling-shutter orthogonal frequency-division multiplexing is used for the high-frequency signals, and M-ary frequency-shift keying is used for the low-frequency signals in the time domain. This proposed scheme is compatible with smartphone and USB cameras. By controlling the OCC parameters, the hybrid scheme can be implemented with high performance for a communication distance of 10 m.

Energy-Band Engineering By Remote Doping of Self-Assembled Monolayers Leads to High-Performance Igzo/P-Si Heterostructure Photodetectors

Advancements in emerging technologies, such as autonomous driving, artificial intelligence, medical imaging, and augmented reality, have increased the demand for high-resolution image sensors to transmit further accurate color information. To meet the performance requirements of these applications, several research have been accelerating towards the utilization of emerging low-dimensional semiconductor materials, such as metal oxides, transition metal dichalcogenides, perovskites, and other organic materials.Among them, metal oxide semiconductors (MOSs) are potential candidates for developing high-performance photodetectors. It can be synthesized over large areas using a simple/low-temperature process and are compatible with conventional fabrication equipment. However, MOS-based photodetectors still have several limitations, 1) the presence of charge traps and operational instability on the oxide surface, which degrade photodetection performance, and 2) the absence of an appropriate doping technique for MOS has blocked to improve photodetection performance via materials characteristic modification method.In this study, we present a strategy to overcome these limitations and significantly enhance the performance of IGZO, which is representative of oxide semiconductor thin film, based photodetector with high operation stability and sustainability under ambient atmospheres. We utilized energy-band engineering through an octadecylphosphonic acid (ODPA) self-assembled-monolayer (SAM)-based doping treatment to enhance the photoelectric characteristics of IGZO/p-Si hetero-interfaced devices (ISH). The ODPA consists of a head group of the hydroxyl group, which forms the self-assembled covalent bond formation of R–P–O–substrate. Combining the ODPA SAM with the oxide surface of IGZO provided carrier concentration control based on their a dipole effect, and remain the performance and functionality through the surface encapsulation. As a result, under a light-emitting diode (LED) wavelength with an intensity of 3.9 mW, the proposed device exhibited remarkable photo-switching current ratio of 4.33 × 103 A/A and negative differential resistance (NDR) phenomenon. Espacially, the dark current of the ODPA treated ISH is decreased about 10 times lower than as-fabricated ISH, which shows the possibility of signal noise minimization. These performance improvement is expected to be derived from band properties modulation through IGZO layer annealing and ODPA doping. To clarify the doping effect of each treatment, we characterized the ODPA self-assembled monolayer using X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and Kelvin probe force microscopy (KPFM) to provide comprehensive analysis of the heterointerface effects. We also characterized the electrical properties of IGZO thin-film transistors (TFTs) based on variations of the annealing temperature to clarify the ODPA doping effect.We evaluated 156 ODPA-treated IGZO/p-Si heterostructure photodetectors, which were scaled for fabrication on a 4 inch wafer. The devices exhibited a 100% yield, and uniform current behavior in the dark state (0.20 ± 0.06 nA) and light state (0.87 ± 0.15 µA), owing to an evenly deposited IGZO layer and a simple-to-treat ODPA doping scheme. Furthermore, consecutive sweeps of 20,000 cycles verified excellent cycle-to-cycle endurance, resulting in highly robust photoelectric behaviors without operational failure. Additionally, the performance of the ODPA-treated, IGZO/p-Si heterostructure photodetector remained unchanged for 237 days without degradation under atmospheric exposure (relative humidity (RH) = 37–53 % and 20.6–23.1 °C). Acknowledgements This research was supported by funding supports from the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2020R1A2C1101647) and Korea government (MEST) (NRF-2017R1A2B3011222). Figure 1

STRATEGY ON HIGH-DEFINITION POINT CLOUD MAP CREATION FOR AUTONOMOUS DRIVING IN HIGHWAY ENVIRONMENTS

Abstract. In recent years, a lot of researchers have been trying the development of efficient ways to create HD maps with centimeter-level precision. Mobile mapping system (MMS) produce 3D HD point cloud map of the surrounding by integrating navigation (i.e., direct georeferencing or DG process) and high-resolution imaging sensor data. Unfortunately, in partially environments, the provided accuracy of the GNSS system degrades dramatically. In order to constraint the drift and correct the georeferenced point cloud map, ground control points (GCPs) are placed along the road. Moreover, there are approaches which use laser-based point cloud registration techniques to construct the point cloud map. However, all promising mapping techniques which currently use the laser as the core sensor for mapping the high-definition point cloud map may not be promised to construct the point cloud map in partially or unfriendly environments. As a literature review and result, a suitable approach for creating the promising point cloud map is to combine the INS/GNSS navigation solution, LiDAR matching techniques, and GCPs. Thus, this study introduces the HD point cloud map generation method that can potentially help researchers create personalized and globalized HD point cloud maps and develop new HD point cloud map generation methodologies.

Anti-Aliasing and Anti-Color-Artifact Demosaicing for High-Resolution CMOS Image Sensor

Anti-Aliasing and Anti-Color-Artifact Demosaicing for High-Resolution CMOS Image Sensor

A 3.06 μm Single-Photon Avalanche Diode Pixel with Embedded Metal Contact and Power Grid on Deep Trench Pixel Isolation for High-Resolution Photon Counting.

In this study, a 3.06 μm pitch single-photon avalanche diode (SPAD) pixel with an embedded metal contact and power grid on two-step deep trench isolation in the pixel is presented. The embedded metal contact can suppress edge breakdown and reduce the dark count rate to 15.8 cps with the optimized potential design. The embedded metal for the contact is also used as an optical shield and a low crosstalk probability of 0.4% is achieved, while the photon detection efficiency is as high as 57%. In addition, the integration of a power grid and the polysilicon resistor on SPAD pixels can help to reduce the voltage drop in anode power supply and reduce the power consumption with SPAD multiplication, respectively, in a large SPAD pixel array for a high-resolution photon-counting image sensor.

A Comprehensive Methodology for Optimizing Read-Out Timing and Reference DAC Offset in High Frame Rate Image Sensing Systems.

This paper presents a comprehensive timing optimization methodology for power-efficient high-resolution image sensors with column-parallel single-slope analog-to-digital converters (ADCs). The aim of the method is to optimize the read-out timing for each period in the image sensor's operation, while considering various factors such as ADC decision time, slew rate, and settling time. By adjusting the ramp reference offset and optimizing the amplifier bandwidth of the comparator, the proposed methodology minimizes the power consumption of the amplifier array, which is one of the most power-hungry circuits in the system, while maintaining a small color linearity error and ensuring optimal performance. To demonstrate the effectiveness of the proposed method, a power-efficient 108 MP 3-D stacked CMOS image sensor with a 10-bit column-parallel single-slope ADC array was implemented and verified. The image sensor achieved a random noise of 1.4 e-rms, a column fixed-pattern noise of 66 ppm at an analog gain of 16, and a remarkable figure-of-merit (FoM) of 0.71 e-·nJ. The sensor utilized a one-row read-out time of 6.9 µs, an amplifier bandwidth of 1.1 MHz, and a reference digital-to-analog converter (DAC) offset of 512 LSB. This timing optimization methodology enhances energy efficiency in high-resolution image sensors, enabling higher frame rates and improved system performance. It could be adapted for various imaging applications requiring optimized performance and reduced power consumption, making it a valuable tool for designers aiming to achieve optimal performance in power-sensitive applications.

A compact model for the well-depleted 1-T pixel sensor based on silicon-on-insulator technology

The silicon-on-I_nsulator (SOI) based one transistor (1-T) pixel sensor is a promising candidate for a high-resolution image sensor and in-sensor computing. In this work, a compact model for the photoresponse of a well-depleted SOI 1-T pixel sensor is proposed from the perspective of the physical process. The model explains the photoelectron accumulation, photoelectron redistribution, and modulation of the transistor according to the electron drift/diffusion mechanism, capacitor–voltage relationship, and current solution from BSIM-IMG. The model agrees well with the experimental results from the 1-T pixel sensor fabricated by the 22 nm fully depleted SOI technology, as well as the simulation results from technology computer-aided design. The extraction scheme of the model parameters is also provided to facilitate the use of the model.

High-performance photodetectors based on two-dimensional perovskite crystals with alternating interlayer cations

Organic-inorganic halide perovskite, as a low-cost, solution-processable material with remarkable optoelectronic properties, is ideal candidate to fabricate high-performance photodetectors and is expected to significantly reduce device costs. Compared to the common Dion-Jacobson and Ruddlesden-Popper two-dimensional (2D) layered hybrid perovskite compounds, the perovskites with alternating cations in the interlayer (ACI) phase show higher crystal symmetry and narrower optical bandgaps, which exhibit great potential for excellent photodetection performance. Herein, we report a high-performance photodetector based on the 2D bilayered hybrid lead halide perovskite single crystal with the ACI phase (GAMA2Pb2I7; GA = C(NH2)3 and MA = CH3NH3). The single-crystal photodetector exhibits high photoresponsivity of 1.56, 2.54, and 2.60 A/W for incident light wavelengths of 405, 532, and 635 nm under 9.82 nW, respectively, together with the correspondingly high detectivity values of 1.86 × 1012, 3.04 × 1012, and 3.11 × 1012 Jones under the same operating conditions. Meanwhile, a high-resolution imaging sensor is built based on the GAMA2Pb2I7 single-crystal photodetector, confirming the high stability and photosensitivity of the imaging system. These results show that the 2D hybrid lead halide perovskites with alternating interlayer cations are promising for high-performance visible light photodetectors and imaging systems.

Longwave infrared multispectral image sensor system using aluminum-germanium plasmonic filter arrays

A multispectral camera records image data in various wavelengths across the electromagnetic spectrum to acquire additional information that a conventional camera fails to capture. With the advent of high-resolution image sensors and color filter technologies, multispectral imagers in the visible wavelengths have become popular with increasing commercial viability in the last decade. However, multispectral imaging in longwave infrared (LWIR, 8–14 μm) is still an emerging area due to the limited availability of optical materials, filter technologies, and high-resolution sensors. Images from LWIR multispectral cameras can capture emission spectra of objects to extract additional information that a human eye fails to capture and thus have important applications in precision agriculture, forestry, medicine, and object identification. In this work, we experimentally demonstrate an LWIR multispectral image sensor with three wavelength bands using optical elements made of an aluminum (Al)-based plasmonic filter array sandwiched in germanium (Ge). To realize the multispectral sensor, the filter arrays are then integrated into a three-dimensional (3D) printed wheel stacked on a low-resolution monochrome thermal sensor. Our prototype device is calibrated using a blackbody and its thermal output has been enhanced with computer vision methods. By applying a state-of-the-art deep learning method, we have also reconstructed multispectral images to a better spatial resolution. Scientifically, our work demonstrates a versatile spectral thermography technique for detecting target signatures in the LWIR range and other advanced spectral analyses.

Multi-image hybrid super-resolution reconstruction via interpolation and multi-scale residual networks

High spatial resolution is necessary for several applications such as visual inspection, and can be achieved using high-resolution (HR) image sensors or through image super-resolution (SR) algorithms. Currently, SR algorithms are applied to either single low-resolution (LR) images or multiple LR image sequences. In this paper, we propose a hybrid super-resolution (HYSR) framework to generate HR images by combining multi-image super-resolution (MISR) and single-image super-resolution (SISR) to obtain high spatial resolution images. This method comprehensively utilizes sub-pixel-level high-frequency detail information between multiple images and co-occurrence prior of a single image to reconstruct SR images with a larger scale factor than the existing methods. Generally, the HYSR reconstruction results have more satisfactory details and visual quality than the SISR or MISR reconstruction results. A large number of qualitative and quantitative evaluation results demonstrate the effectiveness and superiority of the HYSR method over traditional MISR and SISR methods.

Artificial intelligence and automation in endoscopy and surgery.

Modern endoscopy relies on digital technology, from high-resolution imaging sensors and displays to electronics connecting configurable illumination and actuation systems for robotic articulation. In addition to enabling more effective diagnostic and therapeutic interventions, the digitization of the procedural toolset enables video data capture of the internal human anatomy at unprecedented levels. Interventional video data encapsulate functional and structural information about a patient's anatomy as well as events, activity and action logs about the surgical process. This detailed but difficult-to-interpret record from endoscopic procedures can be linked to preoperative and postoperative records or patient imaging information. Rapid advances in artificial intelligence, especially in supervised deep learning, can utilize data from endoscopic procedures to develop systems for assisting procedures leading to computer-assisted interventions that can enable better navigation during procedures, automation of image interpretation and robotically assisted tool manipulation. In this Perspective, we summarize state-of-the-art artificial intelligence for computer-assisted interventions in gastroenterology and surgery.

Acid-Base Reaction-Assisted Quantum Dot Patterning via Ligand Engineering and Photolithography.

The integration of quantum dots (QDs) into device arrays for high-resolution display and imaging sensor systems remains a significant challenge in research and industry because of issues associated with the QD patterning process. It is difficult for conventional patterning processes such as stamping, inkjet printing, and photolithography to employ QDs and fabricate high-resolution patterns without degrading the properties of QDs. Here, we introduce a novel strategy for the QD patterning process by treating QDs with a bifunctional ligand for acid-base reaction-assisted photolithography. Bifunctional ligands, such as MPA (mercaptopropionic acid) or TGA (thioglycolic acid), have a carboxyl group on one side that allows the QDs to be etched along with the photoresist (PR) by the base developer, while on the opposite side the ligands have a thiol group that passivates the QD surface. Passivating MPA ligands on QDs facilitates patterning of QD films and makes them compatible with harsh photolithography processes. We successfully achieved the patterning of QDs down to 5 μm. We also fabricated high-resolution patterned QD light-emitting diodes (LEDs) and QD photodetector arrays. Our patterning process provides precise control for the fabrication of highly integrated QD-based optoelectronic devices.

Robust OCC System Optimized for Low-Frame-Rate Receivers

Light emitting diodes (LED) are becoming the dominant lighting elements due to their efficiency. Optical camera communications (OCC), the branch of visible light communications (VLC) that uses video cameras as receivers, is a suitable candidate in facilitating the development of new communication solutions for the broader public because video cameras are available on almost any smartphone nowadays. Unfortunately, most OCC systems that have been proposed until now require either expensive and specialized high-frame-rate cameras as receivers, which are unavailable on smartphones, or they rely on the rolling shutter effect, being sensitive to camera movement and pointing direction, they produce light flicker when low-frame-rate cameras are used, or they must discern between more than two light intensity values, affecting the robustness of the decoding process. This paper presents in detail the design of an OCC system that overcomes these limitations, being designed for receivers capturing 120 frames per second and being easily adaptable for any other frame rate. The system does not rely on the rolling shutter effect, thus making it insensitive to camera movement during frame acquisition and less demanding about camera resolution. It can work with reflected light, requiring neither a direct line of sight to the light source nor high resolution image sensors. The proposed communication is invariant to the moment when the transmitter and the receiver are started as the communication is self-synchronized, without any other exchange of information between the transmitter and the receiver, without producing light flicker, and requires only two levels of brightness to be detected (light on and light off). The proposed system overcomes the challenge of not producing light flicker even when it is adapted to work with very low-frame-rate receivers. This paper presents the statistical analysis of the communication performance and discusses its implementation in an indoor localization system.

Performance Assessment of the CCSDS-123 Standard for Panchromatic Video Compression on Space Missions

The use of next-generation and high-resolution imaging sensors is gaining interest for space missions, because of their properties for identification and exploration purposes. It is expected that the demand for video sensors in the space industry will increase during the next years, mainly for monitoring and exploration missions. In this context, onboard video compression techniques emerge as mandatory, because memory resources are available on space missions and the downlink bandwidth with ground prevents for the transmission of raw video, especially when near real-time capabilities are required. The complexity of the current commercial video encoders supposes a challenge for their implementation on the hardware used on space missions, because of their high architectural complexity and computational requirements, deriving in an area overhead and an unacceptable power consumption. In this context, we propose the use of the CCSDS-123.0-B-2 standard, originally focused on the near-lossless on-board compression of multispectral and hyperspectral images, for compressing panchromatic video. The proposed solution presents low complexity, because it is specifically designed for working on space missions and also has the capability of compressing both 3-D images and panchromatic video by using a single processing core. Results demonstrate that this solution achieves high compression ratios, maintaining a considerable video quality after reconstruction on ground and achieving a fine-grain control of the losses introduced in the compression chain.