Carolina Blanch

Initial memory complexity analysis of the AVC codec

The Advanced Video Codec (AVC), currently being defined in a joined standardisation effort of ISO/IEC MPEG and ITU-T VCEG, aims at enhanced compression efficiency and network friendliness. To achieve these goals, a motion compensated hybrid DCT algorithm is introduced using advanced and complicated compression tools. As video coding is typically a data dominated process, we quantify the complexity cost in a memory centric way. The AVC codec is characterised by a large memory footprint and increased data transfer rate (an order of magnitude for the encoder) compared to previous video coding standards. The motion estimation/compensation are the initial implementation bottlenecks.

A tiny VIS-NIR snapshot multispectral camera

Spectral imaging can reveal a lot of hidden details about the world around us, but is currently confined to laboratory environments due to the need for complex, costly and bulky cameras. Imec has developed a unique spectral sensor concept in which the spectral unit is monolithically integrated on top of a standard CMOS image sensor at wafer level, hence enabling the design of compact, low cost and high acquisition speed spectral cameras with a high design flexibility. This flexibility has previously been demonstrated by imec in the form of three spectral camera architectures: firstly a high spatial and spectral resolution scanning camera, secondly a multichannel snapshot multispectral camera and thirdly a per-pixel mosaic snapshot spectral camera. These snapshot spectral cameras sense an entire multispectral data cube at one discrete point in time, extending the domain of spectral imaging towards dynamic, video-rate applications. This paper describes the integration of our per-pixel mosaic snapshot spectral sensors inside a tiny, portable and extremely user-friendly camera. Our prototype demonstrator cameras can acquire multispectral image cubes, either of 272x512 pixels over 16 bands in the VIS (470-620nm) or of 217x409 pixels over 25 bands in the VNIR (600-900nm) at 170 cubes per second for normal machine vision illumination levels. The cameras themselves are extremely compact based on Ximea xiQ cameras, measuring only 26x26x30mm, and can be operated from a laptop-based USB3 connection, making them easily deployable in very diverse environments.

Generalized inpainting method for hyperspectral image acquisition

A recently designed hyperspectral imaging device enables multiplexed acquisition of an entire data volume in a single snapshot thanks to monolithically-integrated spectral filters. Such an agile imaging technique comes at the cost of a reduced spatial resolution and the need for a demosaicing procedure on its interleaved data. In this work, we address both issues and propose an approach inspired by recent developments in compressed sensing and analysis sparse models. We formulate our superresolution and demosaicing task as a 3-D generalized inpainting problem. Interestingly, the target spatial resolution can be adjusted for mitigating the compression level of our sensing. The reconstruction procedure uses a fast greedy method called Pseudo-inverse IHT. We also show on simulations that a random arrangement of the spectral filters on the sensor is preferable to regular mosaic layout as it improves the quality of the reconstruction. The efficiency of our technique is demonstrated through numerical experiments on both synthetic and real data as acquired by the snapshot imager.

A CMOS-compatible, monolithically integrated snapshot-mosaic multispectral imager

Imec is a research centre located in Belgium. Specialising in nanoelectronics, it is mostly known for advanced lithography and CMOS scaling research. However, building on that equipment and material knowledge, Imec works in a number of different application-oriented domains. Hyperspectral imaging, to which this article is devoted, is one of them.

Towards a colony counting system using hyperspectral imaging

Colony counting is a procedure used in microbiology laboratories for food quality monitoring, environmental management, etc. Its purpose is to detect the level of contamination due to the presence and growth of bacteria, yeasts and molds in a given product. Current automated counters require a tedious training and setup procedure per product and bacteria type and do not cope well with diversity. This contrasts with the setting at microbiology laboratories, where a wide variety of food and bacteria types have to be screened on a daily basis. To overcome the limitations of current systems, we propose the use of hyperspectral imaging technology and examine the spectral variations induced by factors such as illumination, bacteria type, food source and age and type of the agar. To this end, we perform experiments making use of two alternative hyperspectral processing pipelines and compare our classification results to those yielded by color imagery. Our results show that colony counting may be automated through the automatic recovery of the illuminant power spectrum and reflectance. This is consistent with the notion that the recovery of the illuminant should minimize the variations in the spectra due to reflections, shadows and other photometric artifacts. We also illustrate how, with the reflectance at hand, the colonies can be counted making use of classical segmentation and classification algorithms.

Rate-distortion-complexity performance analysis of the SVC decoder

The Scalable Video Coding extends the H.264/AVC video coding standard by providing temporal, spatial and quality scalability. A set of new tools, such as key picture and inter-layer prediction are introduced in SVC to improve either rate-distortion performance or error resilience. This paper evaluates the impact of these tools on the performance of an optimized SVC decoder, in terms of rate, distortion, and computational complexity. The results facilitate the decision making process of choosing suitable SVC configurations under different application scenarios.

Cross-layer optimization for the Scalable Video Codec over WLAN

A major limitation for wireless video communication over portable devices is the limited energy supply. For this reason, an efficient energy usage becomes a critical issue. In this paper we focus on the energy minimization of the two main energy consumers at the device: video encoding and wireless communication tasks. For this purpose, we develop a cross-layer approach that explores the tradeoff between coding and communication energies. We then exploit the Power-Rate tradeoffs and flexibility of the Scalable Video Codec. Our results show that by adapting the codec configuration at runtime to the specific scenarios we can save up to 40% of the total energy without video quality loss. Moreover, our approach is of low complexity and easily deployable.

Energy-efficient transmission of H.264 Scalable Video over IEEE 802.11E

Achieving low energy consumption is one of the main challenges for wireless video transmission on battery-limited devices. Moreover, the bandwidth is scarce and must be shared efficiently among users. The focus in this paper is on the timely delivery of multiple delay-sensitive video flows over a distributed access wireless LAN with minimal energy cost. This is done taking into consideration the Enhanced Distributed Channel Access (EDCA) mode and the Scalable Video Codec (SVC). In this context, a method is presented for energy-efficient resource allocation across the physical layer and medium access layer, by properly leveraging transmission modes and the available prioritization mechanisms. Global energy savings around 60% are achieved with respect to state-of-the-art EDCA under a wide range of network loads.

Channel-Aware Rate Adaptation for Energy Optimization and Congestion Avoidance

Achieving low energy consumption is one of the main challenges for wireless video transmission on battery limited devices. Moreover, the bandwidth is scarce and needs to be properly shared amongst different users. Congestion in the network can result in packet losses, with a significant impact on video quality. In this paper we propose the use of a channel-adaptive rate control mechanism in a multi-user WLAN up-link scenario. The benefit is twofold: the communication energy is reduced and congestion is strongly alleviated allowing an increase of the video quality or a network capacity increase for a similar quality.

Multi-objective genetic algorithm downlink resource allocation in LTE: Exploiting the cell-edge vs. Cell-center trade-off

Resource allocation in LTE networks is a challenging problem as multiple users, under a variety of channel conditions, compete for scarce network resources. Moreover, targeting the optimization of the network capacity while still guaranteeing a minimum quality of service complicates the problem considerably. Traditional schedulers can achieve a single tradeoff between the conflicting capacity optimization and fairness objectives. This paper presents a novel allocation strategy based on genetic algorithms in which both objectives are simultaneously optimized. This is done by maximizing both the total cell rate and cell-edge rate. The results show that the proposed algorithm offers a range of Pareto optimal solutions that outperform all other reference strategies.