Compressió d'imatges accelerada

Actualment, la majoria de sistemes de compressió d'imatges i vídeo utilitzen mecanismes que comporten una pèrdua, en molts casos imperceptible, en la qualitat d'imatge a canvi d'obtenir factors de compressió molt elevats. Per minimitzar la pèrdua en la qualitat d'imatge, els compressors utilitzen tècniques d'optimització de taxa-distorsió, les quals són molt costoses computacionalment. L'estudi presentat en aquest treball introdueix estimadors de la distorsió. Aquests estimadors permeten reduir significativament els costos computacionals d'aquestes tècniques sense penalitzar la qualitat d'imatge. Les seves aplicacions són múltiples: acceleren el procés de compressió d'imatges, poden estimar la qualitat en transmissions interactives, o bé poden ajudar en processos de re-codificació a no perdre qualitat.Actualmente la mayoría de sistemas de compresión de imágenes y vídeo utilizan mecanismos que comportan una pérdida, en muchos casos imperceptible, en la calidad de imagen a cambio de obtener factores de compresión muy elevados. Para minimizar la pérdida de calidad de imagen, los compresores utilizan técnicas de optimización de tasa-distorsión, los cuales son muy costosos computacionalmente. El estudio presentado en este trabajo introduce estimadores de la distorsión. Estos estimadores permiten reducir significativamente los costes computacionales de estas técnicas sin penalizar la calidad de imagen. Sus aplicaciones son múltiples: aceleran el proceso de compresión de imágenes, pueden estimar la calidad en transmisiones interactivas, o bien pueden ayudar a no perder calidad en procesos de re-codificación

Fast and Efficient Entropy Coding Architectures for Massive Data Compression

The compression of data is fundamental to alleviating the costs of transmitting and storing massive datasets employed in myriad fields of our society. Most compression systems employ an entropy coder in their coding pipeline to remove the redundancy of coded symbols. The entropy-coding stage needs to be efficient, to yield high compression ratios, and fast, to process large amounts of data rapidly. Despite their widespread use, entropy coders are commonly assessed for some particular scenario or coding system. This work provides a general framework to assess and optimize different entropy coders. First, the paper describes three main families of entropy coders, namely those based on variable-to-variable length codes (V2VLC), arithmetic coding (AC), and tabled asymmetric numeral systems (tANS). Then, a low-complexity architecture for the most representative coder(s) of each family is presented-more precisely, a general version of V2VLC, the MQ, M, and a fixed-length version of AC and two different implementations of tANS. These coders are evaluated under different coding conditions in terms of compression efficiency and computational throughput. The results obtained suggest that V2VLC and tANS achieve the highest compression ratios for most coding rates and that the AC coder that uses fixed-length codewords attains the highest throughput. The experimental evaluation discloses the advantages and shortcomings of each entropy-coding scheme, providing insights that may help to select this stage in forthcoming compression systems

Nova tècnica que optimitza la transmissió de vídeo en xarxa

En l'actualitat existeixen múltiples aplicacions que requereixen de la transmissió de vídeo per Internet: vídeo sota demanda, televisió a la carta, o videoconferència en són alguns exemples presents en la nostra vida quotidiana. Generalment, la compressió i transmissió de vídeo es realitza utilitzant algun dels estàndards internacionals instaurats a les comunitats professionals. Entre ells, l'estàndard JPEG2000 destaca per ser utilitzat en entorns de producció de televisió i cinema digital. Un aspecte fonamental en la transmissió de vídeo per la xarxa és maximitzar la qualitat de la imatge transmesa. Per aconseguir aquest objectiu, s'utilitzen els anomenats mètodes d'assignació de taxa. L'estudi presentat en aquest treball proposa un mètode d'assignació de taxa per a la transmissió de vídeo JPEG2000 que aconsegueix una qualitat gairebé òptima requerint recursos computacionals gairebé nuls.En la actualidad existen múltiples aplicaciones que requieren de la transmisión de vídeo por Internet: vídeo bajo demanda, televisión a la carta, o videoconferencia son algunos ejemplos presentes en nuestra vida cotidiana. Generalmente, la compresión y transmisión de vídeo se realiza utilizando alguno de los estándares internacionales instaurados en las comunidades profesionales. Entre ellos, el estándar JPEG2000 destaca por ser utilizado en entornos de producción de televisión y cine digital. Un aspecto fundamental en la transmisión de vídeo por la red es maximizar la calidad de la imagen transmitida. Para conseguir este objetivo, se utilizan los llamados métodos de asignación de tasa. El estudio presentado en este trabajo propone un método de asignación de tasa para la transmisión de vídeo JPEG2000 que alcanza una calidad casi óptima requiriendo recursos computacionales casi nulos

Entropy-based evaluation of context models for wavelet-transformed images

Entropy is a measure of a message uncertainty. Among others aspects, it serves to determine the minimum coding rate that practical systems may attain. This paper defines an entropy-based measure to evaluate context models employed in wavelet-based image coding. The proposed measure is defined considering the mechanisms utilized by modern coding systems. It establishes the maximum performance achievable with each context model. This helps to determine the adequateness of the model under different coding conditions and serves to predict with high precision the coding rate achieved by practical systems. Experimental results evaluate four well-known context models using different types of images, coding rates, and transform strategies. They reveal that, under specific coding conditions, some widely-spread context models may not be as adequate as it is generally thought. The hints provided by this analysis may help to design simpler and more efficient wavelet-based image codecs

Context-adaptive binary arithmetic coding with fixed-length codewords

Context-adaptive binary arithmetic coding is a widespread technique in the field of image and video coding. Most state-of-the-art arithmetic coders produce a (long) codeword of a priori unknown length. Its generation requires a renormalization procedure to permit progressive processing. This paper introduces two arithmetic coders that produce multiple codewords of fixed length. Contrary to the traditional approach, the generation of fixed-length codewords does not require renormalization since the whole interval arithmetic is stored in the coder's internal registers. The proposed coders employ a new context-adaptive mechanism based on variable-size sliding window that estimates with high precision the probability of the symbols coded. Their integration in coding systems is straightforward as demonstrated within the framework of JPEG2000. Experimental tests indicate that the proposed coders are computationally simpler than the MQ coder of JPEG2000 and the M coder of HEVC while achieving superior coding efficiency

Qualitat d'imatge de JPEG2000 millorada

L'objectiu d'aquest treball és la millora de la qualitat d'imatges comprimides amb l'estàndard JPEG2000. JPEG2000 és un estàndard ISO/IEC que ha estat desenvolupat durant més de 10 anys per universitats i empreses d'arreu del món, i es considera estat de l'art. Actualment és utilitzat en centres i institucions on es treballa massivament amb imatges, com hospitals, centres de teledetecció, i empreses de producció i edició de vídeos, entre d'altres.El objetivo de este trabajo es la mejora de la calidad de imágenes comprimidas con el estándar JPEG2000. JPEG2000 es un estándar ISO/IEC que ha sido desarrollado durante más de 10 años por universidades y empresas de todo el mundo, y se considera estado del arte. Actualmente es utilizado en centros e instituciones donde se trabaja masivamente con imágenes, como hospitales, centros de teledetección, y empresas de producción y edición de vídeos, entre otros

General embedded quantization for wavelet-based lossy image coding

Embedded quantization is a mechanism employed by many lossy image codecs to progressively refine the distortion of a (transformed) image. Currently, the most common approach to do so in the context of wavelet-based image coding is to couple uniform scalar deadzone quantization (USDQ) with bitplane coding (BPC). USDQ+BPC is convenient for its practicality and has proved to achieve competitive coding performance. But the quantizer established by this scheme does not allow major variations. This paper introduces a multistage quantization scheme named general embedded quantization (GEQ) that provides more flexibility to the quantizer. GEQ schemes can be devised for specific decoding rates achieving optimal coding performance. Practical approaches of GEQ schemes achieve coding performance similar to that of USDQ+BPC while requiring fewer quantization stages. The performance achieved by GEQ is evaluated in this paper through experimental results carried out in the framework of modern image coding systems

2-step scalar deadzone quantization for bitplane image coding

Modern lossy image coding systems generate a quality progressive codestream that, truncated at increasing rates, produces an image with decreasing distortion. Quality progressivity is commonly provided by an embedded quantizer that employs uniform scalar deadzone quantization (USDQ) together with a bitplane coding strategy. This paper introduces a 2-step scalar deadzone quantization (2SDQ) scheme that achieves same coding performance as that of USDQ while reducing the coding passes and the emitted symbols of the bitplane coding engine. This serves to reduce the computational costs of the codec and/or to code high dynamic range images. The main insights behind 2SDQ are the use of two quantization step sizes that approximate wavelet coefficients with more or less precision depending on their density, and a rate-distortion optimization technique that adjusts the distortion decreases produced when coding 2SDQ indexes. The integration of 2SDQ in current codecs is straightforward. The applicability and efficiency of 2SDQ are demonstrated within the framework of JPEG2000

DPCM-based edge prediction for lossless screen content coding in HEVC

Screen content sequences are ubiquitous type of video data in numerous multimedia applications like video conferencing, remote education, and cloud gaming. These sequences are characterized for depicting a mix of computer generated graphics, text, and camera-captured material. Such a mix poses several challenges, as the content usually depicts multiple strong discontinuities, which are hard to encode using current techniques. Differential pulse code modulation (DPCM)-based intra-prediction has shown to improve coding efficiency for these sequences. In this paper we propose sample-based edge and angular prediction (SEAP), a collection of DPCM-based intra-prediction modes to improve lossless coding of screen content. SEAP is aimed at accurately predicting regions depicting not only camera-captured material, but also those depicting strong edges. It incorporates modes that allow selecting the best predictor for each pixel individually based on the characteristics of the causal neighborhood of the target pixel. We incorporate SEAP into HEVC intra-prediction. Evaluation results on various screen content sequences show the advantages of SEAP over other DPCM-based approaches, with bit-rate reductions of up to 19.56% compared to standardized RDPCM. When used in conjunction with the coding tools of the screen content coding extensions, SEAP provides bit-rate reductions of up to 8.63% compared to RDPCM

Stationary probability model for microscopic parallelism in JPEG2000

Parallel processing is key to augmenting the throughput of image codecs. Despite numerous efforts to parallelize wavelet-based image coding systems, most attempts fail at the parallelization of the bitplane coding engine, which is the most computationally intensive stage of the coding pipeline. The main reason for this failure is the causality with which current coding strategies are devised, which assumes that one coefficient is coded after another. This work analyzes the mechanisms employed in bitplane coding and proposes alternatives to enhance opportunities for parallelism. We describe a stationary probability model that, without sacrificing the advantages of current approaches, removes the main obstacle to the parallelization of most coding strategies. Experimental tests evaluate the coding performance achieved by the proposed method in the framework of JPEG2000 when coding different types of images. Results indicate that the stationary probability model achieves similar coding performance, with slight increments or decrements depending on the image type and the desired level of parallelism