Ultrafast and Efficient Scalable Image Compression Algorithm

Wavelet-based image compression algorithms have good performance and produce a rate scalable bitstream that can be decoded efficiently at several bit rates. Unfortunately, the discrete wavelet transform (DWT) has relatively high computational complexity. On the other hand, the discrete cosine transform (DCT) has low complexity and excellent compaction properties. Unfortunately, it is non-local, which necessitates implementing it as a block-based transform leading to the well-known blocking artifacts at the edges of the DCT blocks. This paper proposes a very fast and rate scalable algorithm that exploits the low complexity of DCT and the low complexity of the set partitioning technique used by the wavelet-based algorithms. Like JPEG, the proposed algorithm first transforms the image using block-based DCT. Then, it rearranges the DCT coefficients into a wavelet-like structure. Finally, the rearranged image is coded using a modified version of the SPECK algorithm, which is one of the best well-known wavelet-based algorithms. The modified SPECK consumes slightly less computer memory, has slightly lower complexity and slightly better performance than the original SPECK. The experimental results demonstrated that the proposed algorithm has competitive performance and high processing speed. Consequently, it has the best performance to complexity ratio among all the current rate scalable algorithms

Underwater Wireless Vision System Using Progressive Image Compression and Region of Interest

[ES] La creciente demanda en todo el mundo de sistemas de intervención submarina en diversos dominios de aplicación requiere sistemas más versátiles y económicos. Empleando un sistema de comunicación inalámbrica, los robots semiautónomos supervisados disponen de libertad de movimientos y, al mismo tiempo, permiten al operador obtener información visual y supervisar la intervención. Por otro lado, la velocidad de transmisión de datos típica de los canales inalámbricos submarinos es, en general, muy limitada, siendo necesaria la aplicación de técnicas de compresión avanzadas. En este artículo se presenta principalmente el algoritmo DEBT (Depth Embedded Block Tree) para la compresión progresiva de imágenes con región de interés (ROI). Los resultados demuestran ventajas con respecto a otros algoritmos de compresión, y la posibilidad de ejecución del algoritmo en tiempo real en ordenadores embebidos de bajo consumo basados en ARM.[EN] The increasing demand for underwater robotic intervention systems around the world in several application domains requires more versatile and inexpensive systems. By using a wireless communication system, supervised semi-autonomous robots have freedom of movement and, at the same time, allows the operator to get camera feedback and supervise the intervention. On the otherhand, the typical data rate of the wireless submarine channels is generally very limited, requiring the application of advanced compression techniques. In this paper we present the DEBT (Depth Embedded Block Tree) algorithm for the progressive compressionof images with region of interest (ROI). The results demonstrate advantages with other compression algorithms, and the possibilityof executing the algorithm in real time on ARM-based embedded low-power computers.Este trabajo ha sido parcialmente financiado por el Ministerio de Economía y competitividad, código de proyecto DPI2014-57746-C3 (proyecto MERBOTS), por la Generalitat Valenciana GVA, con el código de proyecto PROMETEO/2016/066 y por la Universidad Jaume I, proyecto MASUMIA (P1-1B2015-68), becas PREDOC/2012/47, PREDOC/2013/46, y por el CNPq del Brasil.Rubino, EM.; Centelles, D.; Sales, J.; Martí, JV.; Marín, R.; Alvares, AJ.; Sanz, PJ. (2017). Sistema de Visión Subacuático Inalámbrico Usando un Algoritmo de Compresión Progresivo con Región de Interés. Revista Iberoamericana de Automática e Informática industrial. 15(1):46-57. https://doi.org/10.4995/riai.2017.8953OJS4657151Adams, M., Kossentini, F., Sept 2000. Jasper: a software-based JPEG-2000 codec implementation. In: Image Processing, 2000. 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