The protection of digital images and video from unauthorized access is important for a number of applications, including privacy protection in video surveillance and digital rights management for consumer applications. However, traditional cryptographic methods are not well suited to digital visual content. Applying standard encryption approaches to the entire content can require significant computational resources due to the large size of the data. Furthermore, digital images and video often need to be manipulated,such as by resizing or transcoding, which traditional encryption would hinder. A number of image and video-specific encryption approaches have been proposed in the literature, but many of the them have significant negative impact on the ability to compress the data, which is a necessary requirement of most imaging systems. In this work, a secure image coder, called Secure Set Partitioning in Hierarchical Trees (SecSPIHT), is proposed. It combines wavelet-based image coding (compression) with efficient encryption. The encryption is applied to a small number of selected bits in the code domain, to achieve complete confidentiality of all the content while having no negative impact on compression performance. The output of the system is a secure code that cannot be decrypted and decoded without the provision of a secret key. It has superior rate-distortion performance compared to JPEG and JPEG2000, and the bit-rate can be easily scaled via a simple truncation operation. The computational overhead of the encryption operation is very low, typically requiring less than 1% of the coded image data to be encrypted. A related secure object-based coding approach is also presented. Called Secure Shape and Texture Set Partitioning in Hierarchical Trees (SecST-SPIHT), it codes and encrypts arbitrarily-shaped visual objects. A privacy protection system for video surveillance is proposed, using SecST-SPIHT to protect private data, such as face and body images appearing in surveillance footage. During normal operation of the system, the private data objects are protected via SecST-SPIHT. If an incident occurs that requires access to the data (e.g., for investigation), a designated authority must release the key. This is superior to other methods of privacy protection which irreversibly blur or mask the private data.

The protection of digital images and video from unauthorized access is important for a number of applications, including privacy protection in video surveillance and digital rights management for consumer applications. However, traditional cryptographic methods are not well suited to digital visual content. Applying standard encryption approaches to the entire content can require significant computational resources due to the large size of the data. Furthermore, digital images and video often need to be manipulated,such as by resizing or transcoding, which traditional encryption would hinder. A number of image and video-specific encryption approaches have been proposed in the literature, but many of the them have significant negative impact on the ability to compress the data, which is a necessary requirement of most imaging systems. In this work, a secure image coder, called Secure Set Partitioning in Hierarchical Trees (SecSPIHT), is proposed. It combines wavelet-based image coding (compression) with efficient encryption. The encryption is applied to a small number of selected bits in the code domain, to achieve complete confidentiality of all the content while having no negative impact on compression performance. The output of the system is a secure code that cannot be decrypted and decoded without the provision of a secret key. It has superior rate-distortion performance compared to JPEG and JPEG2000, and the bit-rate can be easily scaled via a simple truncation operation. The computational overhead of the encryption operation is very low, typically requiring less than 1% of the coded image data to be encrypted. A related secure object-based coding approach is also presented. Called Secure Shape and Texture Set Partitioning in Hierarchical Trees (SecST-SPIHT), it codes and encrypts arbitrarily-shaped visual objects. A privacy protection system for video surveillance is proposed, using SecST-SPIHT to protect private data, such as face and body images appearing in surveillance footage. During normal operation of the system, the private data objects are protected via SecST-SPIHT. If an incident occurs that requires access to the data (e.g., for investigation), a designated authority must release the key. This is superior to other methods of privacy protection which irreversibly blur or mask the private data.

[pt] Codificação de objetos de forma arbitrária extraídos de imagens regulares é um dos assuntos de maior desafio nos mais recentes padrões de codificação de vídeo (MPEG-4). Neste estudo, é proposta uma nova estratégia de codificação de objetos envolvendo maior eficiência na decomposição e quantização de sub-bandas. A técnica de transformação proposta envolve Transformada Wavelet Discreta (DWT) e a de quantização é baseada no algoritmo de Partição de Conjuntos em Árvores Hierárquicas (SPIHT). / [en] Coding of arbitrarily shaped objects extracted from regular images is one of the most challenging issues on the latest video coding standards (MPEG-4). In this work, a new object coding strategy is proposed, involving greater efficiency on subband splitting and quantization. The proposed transform technique involves Discrete Wavelet Transform (DWT) and the proposed quantization technique is based on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. / [es] La codificación de objetos de forma arbitraria extraídos de imágenes regulares es uno de los asuntos de mayor desafío en los más recientes padrones de codificación de video (MPEG-4). En este estudio, se propone una nueva estrategia de codificación de objetos con mayor eficiencia en la descomposición y cuantización de subbandas. La técnica de transformación propuesta incluye Transformada Wavelet Discreta (DWT) y la de cuantización tiene como base el algoritmo de Partición de Conjuntos en Árboles Jerárquicas (SPIHT).