Visually lossless coding allows image codecs to achieve high compression ratios while producing images without visually noticeable distortion. In general, visually lossless coding is approached from the point of view of the encoder, so most methods are not applicable to already compressed codestreams. This paper presents two algorithms focused on the visually lossless decoding and transmission of JPEG2000 codestreams. The proposed strategies can be employed by a decoder, or a JPIP server, to reduce the decoding or transmission rate without penalizing the visual quality of the resulting images.

Visually lossless coding is a method through which an image is coded with numerical losses that are not noticeable by visual inspection. Contrary to numerically lossless coding, visually lossless coding can achieve high compression ratios. In general, visually lossless coding is approached from the point of view of the encoder, i.e., as a procedure devised to generate a compressed code stream from an original image. If an image has already been encoded to a very high fidelity (higher than visually lossless - perhaps even numerically lossless), it is not straightforward to create a just visually lossless version without fully re-encoding the image. However, for large repositories, re-encoding may not be a suitable option. A visually lossless decoder might be useful to decode, or to parse and transmit, only the data needed for visually lossless reconstruction. This work introduces a decoder for JPEG 2000 code streams that identifies and decodes the minimum amount of information needed to produce a visually lossless image. The main insights behind the proposed method are to estimate the variance of the code blocks before the decoding procedure, and to determine the visibility thresholds employing a well-known model from the literature. The main advantages are faster decoding and the possibility to transmit visually lossless images employing minimal bit rates.

This work introduces a rate allocation method for the transmission of pre-encoded JPEG2000 video over time-varying channels, which vary their capacity during video transmission due to network congestion, hardware failures, or router saturation. Such variations occur often in networks and are commonly unpredictable in practice. The optimization problem is posed for such networks and a rate allocation method is formulated to handle such variations. The main insight of the proposed method is to extend the complexity scalability features of the FAst rate allocation through STeepest descent (FAST) algorithm. Extensive experimental results suggest that the proposed transmission scheme achieves near-optimal performance while expending few computational resources.

Embedded quantization is a mechanism employed by lossy image coding systems to successively refine the distortion of an image. Commonly, it is conducted through a uniform scalar dead zone quantizer (USDQ) together with a bitplane coding strategy (BPC). Although this scheme is convenient for current hardware architectures and achieves competitive coding performance, it establishes the embedded quantizer without allowing major variations. This paper studies the design of non-restricted embedded quantizers with the aim to determine a quantization scheme that provides (near-)optimal performance for the lossy compression of images at low rates. Results suggest that optimally designed quantization schemes can achieve slightly better performance than that of USDQ+BPC by employing a non-uniform quantizer that requires fewer quantization stages.

Rate allocation methods are used for variable bit rate (VBR) video transmission. The Fast rate allocation through STepest descent (FAST) is a rate allocation method that achieves valid solutions in scenarios where buffer and bandwidth may vary from client to client, respecting buffer limits and fulfilling real time processing requeriments in a JPEG2000 framework. This work restates the optimization problem and implements an expansion for FAST algorithm, allowing the adaptation to possible changes in channel bandwidth while video still transferring. The proposed method considers buffer's fullness, bandwidth changes and number of frames remainig to transfer in order to provide a valid solution. Experimental results suggest the validity of solutions whereas maintaining the initial version features.