Abstract: According to one method, a method for decoding data using rate sorted entropy coding occurs at a video decoder implemented using at least one processor. The method comprises: receiving rate control information associated with a first bitstream; determining, using the rate control information, a maximum amount of bits available for a next symbol in the first bitstream; determining, using a symbol probability model and a first deterministic algorithm, an amount of bits for a least probable value of the next symbol; and determining that the next symbol is included in the first bitstream if the amount of bits for the least probable value of the next symbol is less than or equal to the maximum amount of bits available for the first bitstream.

Abstract: According to one method, a method for decoding data using rate sorted entropy coding occurs at a video decoder implemented using at least one processor. The method comprises: receiving rate control information associated with a first bitstream; determining, using the rate control information, a maximum amount of bits available for a next symbol in the first bitstream; determining, using a symbol probability model and a first deterministic algorithm, an amount of bits for a least probable value of the next symbol; and determining that the next symbol is included in the first bitstream if the amount of bits for the least probable value of the next symbol is less than or equal to the maximum amount of bits available for the first bitstream.

Abstract: According to one exemplary method, the method includes classifying image pixels in each video image of a series of video images as either foreground or background. The method also includes replacing each pixel classified as background with a time-averaged value of pixel intensity for that pixel. The method further includes compressing the video images using a compression algorithm.

Abstract: Methods, systems, and non-transitory computer readable media for reconstructing electronic images using blurring and noise-enhanced pixel intensity resampling are disclosed. One method includes, at an image reconstructor including at least one processor, receiving an image compressed using an image compression algorithm. The method further includes decompressing the image using an image decompression algorithm. The method further includes modifying pixel intensities in the image by blurring and/or resampling the pixel intensities according to a noise distribution generated by the reconstructor without receiving a noise estimate from an image compressor. The method further includes outputting the image with the modified pixel intensities.

Abstract: Methods, systems, and non-transitory computer readable media for reconstructing electronic images using blurring and noise-enhanced pixel intensity resampling are disclosed. One method includes, at an image reconstructor including at least one processor, receiving an image compressed using an image compression algorithm. The method further includes decompressing the image using an image decompression algorithm. The method further includes modifying pixel intensities in the image by blurring and/or resampling the pixel intensities according to a noise distribution generated by the reconstructor without receiving a noise estimate from an image compressor. The method further includes outputting the image with the modified pixel intensities.

Abstract: Methods, systems, and computer readable media for compressing video images are disclosed. According to one exemplary method, the method includes classifying image pixels in each video image of a series of video images as either foreground or background. The method also includes replacing each pixel classified as background with a time-averaged value of pixel intensity for that pixel. The method further includes compressing the video images using a compression algorithm.