Abstract: Quantization matrices facilitate digital audio encoding and decoding. An audio encoder generates and compresses quantization matrices; an audio decoder decompresses and applies the quantization matrices. For example, the audio encoder generates a quantization matrix including weighting factors and processes a set of weighting factors according to a parametric model to switch between a direct representation and a parametric representation of the set of weighting factors, where the parametric representation of the set of weighting factors accounts for audibility of distortion according to a model of human auditory perception. In another example, an audio encoder receives a band weight representation of a ciuantization matrix and compresses the band weight representation of the quantization matrix using linear predictive coding, wherein the compressing includes computing pseudo-autocorrelation values for the quantization matrix.

Abstract: The coded block parameters used to code blocks of image samples into structures called macroblocks are compressed more efficiently by exploiting the correlation between chrominance and luminance blocks in each macroblock. In particular, the coded block pattern for chrominance and luminance are combined into a single parameter for the macroblock and jointly coded with a single variable length code. To further enhance coding efficiency, the spatial coherence of coded block patterns can be exploited by using spatial prediction to compute predicted values for coded block pattern parameters.

Abstract: An audio processing tool measures the quality of reconstructed audio data. For example, an audio encoder measures the quality of a block of reconstructed frequency coefficient data in a quantization loop. The invention includes several techniques and tools, which can be used in combination or separately. First, before measuring quality, the tool normalizes the block to account for variation in block sizes. Second, for the quality measurement, the tool processes the reconstructed data by critical bands, which can differ from the quantization bands used to compress the data. Third, the tool accounts for the masking effect of the reconstructed data, not just the masking effect of the original data. Fourth, the tool band weights the quality measurement, which can be used to account for noise substitution or band truncation. Finally, the tool changes quality measurement techniques depending on the channel coding mode.

Abstract: An audio processing tool measures the quality of reconstructed audio data. For example, an audio encoder measures the quality of a block of reconstructed frequency coefficient data in a quantization loop. The invention includes several techniques and tools, which can be used in combination or separately. First, before measuring quality, the tool normalizes the block to account for variation in block sizes. Second, for the quality measurement, the tool processes the reconstructed data by critical bands, which can differ from the quantization bands used to compress the data. Third, the tool accounts for the masking effect of the reconstructed data, not just the masking effect of the original data. Fourth, the tool band weights the quality measurement, which can be used to account for noise substitution or band truncation. Finally, the tool changes quality measurement techniques depending on the channel coding mode.

Abstract: The coded block parameters used to code blocks of image samples into structures called macroblocks are compressed more efficiently by exploiting the correlation between chrominance and luminance blocks in each macroblock. In particular, the coded block pattern for chrominance and luminance are combined into a single parameter for the macroblock and jointly coded with a single variable length code. To further enhance coding efficiency, the spatial coherence of coded block patterns can be exploited by using spatial prediction to compute predicted values for coded block pattern parameters.

Abstract: Techniques and tools for processing reference frames in a motion estimation/compensation loop or motion compensation loop are described. For example, a video encoder or decoder filters reference frames to reduce discontinuities at block boundaries, improving the efficiency of motion estimation and compensation.

Abstract: A transcoder reduces excess requantization error in quantization of spectral data. The transcoder phase shifts data decompressed by a decompressor. The phase shifting causes a change to corresponding spectral data produced in later transform coding of the decompressed data. When the spectral data is then quantized to reduce bitrate, the earlier phase shifting reduces excess requantization error. After transcoding, a second decompressor can compensate for the phase shifting by, for example, reverse shifting by the amount of the phase shift. Instead of phase shifting, the transcoder can reduce excess requantization error by, for example, adding random noise to the decompressed data or changing transform block sizes.

Abstract: A semantic object tracking method tracks general semantic objects with multiple non-rigid motion, disconnected components and multiple colors throughout a vector image sequence. The method accurately tracks these general semantic objects by spatially segmenting image regions from a current frame and then classifying these regions as to which semantic object they originated from in the previous frame. To classify each region, the method perform a region based motion estimation between each spatially segmented region and the previous frame to computed the position of a predicted region in the previous frame. The method then classifies each region in the current frame as being part of a semantic object based on which semantic object in the previous frame contains the most overlapping points of the predicted region. Using this method, each region in the current image is tracked to one semantic object from the previous frame, with no gaps or overlaps.

Abstract: A transcoder reduces excess requantization error in quantization of spectral data. The transcoder phase shifts data decompressed by a decompressor. The phase shifting causes a change to corresponding spectral data produced in later transform coding of the decompressed data. When the spectral data is then quantized to reduce bitrate, the earlier phase shifting reduces excess requantization error. After transcoding, a second decompressor can compensate for the phase shifting by, for example, reverse shifting by the amount of the phase shift. Instead of phase shifting, the transcoder can reduce excess requantization error by, for example, adding random noise to the decompressed data or changing transform block sizes.

Abstract: The coded block parameters used to code blocks of image samples into structures called macroblocks are compressed more efficiently by exploiting the correlation between chrominance and luminance blocks in each macroblock. In particular, the coded block pattern for chrominance and luminance are combined into a single parameter for the macroblock and jointly coded with a single variable length code. To further enhance coding efficiency, the spatial coherence of coded block patterns can be exploited by using spatial prediction to compute predicted values for coded block pattern parameters.

Abstract: The coded block parameters used to code blocks of image samples into structures called macroblocks are compressed more efficiently by exploiting the correlation between chrominance and luminance blocks in each macroblock. In particular, the coded block pattern for chrominance and luminance are combined into a single parameter for the macroblock and jointly coded with a single variable length code. To further enhance coding efficiency, the spatial coherence of coded block patterns can be exploited by using spatial prediction to compute predicted values for coded block pattern parameters.

Abstract: An audio encoder regulates quality and bitrate with a control strategy. The strategy includes several features. First, an encoder regulates quantization using quality, minimum bit count, and maximum bit count parameters. Second, an encoder regulates quantization using a noise measure that indicates reliability of a complexity measure. Third, an encoder normalizes a control parameter value according to block size for a variable-size block. Fourth, an encoder uses a bit-count control loop de-linked from a quality control loop. Fifth, an encoder addresses non-monotonicity of quality measurement as a function of quantization level when selecting a quantization level. Sixth, an encoder uses particular interpolation rules to find a quantization level in a quality or bit-count control loop. Seventh, an encoder filters a control parameter value to smooth quality. Eighth, an encoder corrects model bias by adjusting a control parameter value in view of current buffer fullness.

Abstract: An audio encoder regulates quality and bitrate with a control strategy. The strategy includes several features. First, an encoder regulates quantization using quality, minimum bit count, and maximum bit count parameters. Second, an encoder regulates quantization using a noise measure that indicates reliability of a complexity measure. Third, an encoder normalizes a control parameter value according to block size for a variable-size block. Fourth, an encoder uses a bit-count control loop de-linked from a quality control loop. Fifth, an encoder addresses non-monotonicity of quality measurement as a function of quantization level when selecting a quantization level. Sixth, an encoder uses particular interpolation rules to find a quantization level in a quality or bit-count control loop. Seventh, an encoder filters a control parameter value to smooth quality. Eighth, an encoder corrects model bias by adjusting a control parameter value in view of current buffer fullness.

Abstract: Video coding efficiency is improved by jointly coding the x and y components of motion vectors with a single variable length code. The motion vector components for a block of pixels are predicted based on motion vectors of neighboring blocks of pixels. The predicted x and y components are then jointly coded by assigning a single variable length code corresponding to the pair of components, rather than a separate code for each component. If the x and y components do not have a corresponding entry in the coding table, they are coded with an escape code followed by fixed length codes.

Abstract: A transcoder reduces excess requantization error in quantization of spectral data. The transcoder phase shifts data decompressed by a decompressor. The phase shifting causes a change to corresponding spectral data produced in later transform coding of the decompressed data. When the spectral data is then quantized to reduce bitrate, the earlier phase shifting reduces excess requantization error. After transcoding, a second decompressor can compensate for the phase shifting by, for example, reverse shifting by the amount of the phase shift. Instead of phase shifting, the transcoder can reduce excess requantization error by, for example, adding random noise to the decompressed data or changing transform block sizes.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: A semantic object tracking method tracks general semantic objects with multiple non-rigid motion, disconnected components and multiple colors throughout a vector image sequence. The method accurately tracks these general semantic objects by spatially segmenting image regions from a current frame and then classifying these regions as to which semantic object they originated from in the previous frame. To classify each region, the method perform a region based motion estimation between each spatially segmented region and the previous frame to computed the position of a predicted region in the previous frame. The method then classifies each region in the current frame as being part of a semantic object based on which semantic object in the previous frame contains the most overlapping points of the predicted region. Using this method, each region in the current image is tracked to one semantic object from the previous frame, with no gaps or overlaps.

Abstract: An improved loss recovery method for coding streaming media classifies each data unit in the media stream as an independent data unit (I unit), a remotely predicted unit (R unit) or a predicted data unit (P unit). Each of these units is organized into independent segments having an I unit, multiple P units and R units interspersed among the P units. The beginning of each segment is the start of a random access point, while each R unit provides a loss recovery point that can be placed independently of the I unit. This approach separates the random access point from the loss recovery points provided by the R units, and makes the stream more impervious to data losses without substantially impacting coding efficiency. The most important data units are transmitted with the most reliability to ensure that the majority of the data received by the client is usable. The I units are the least sensitive to transmission losses because they are coded using only their own data.

Abstract: Quantization matrices facilitate digital audio encoding and decoding. An audio encoder generates and compresses quantization matrices; an audio decoder decompresses and applies the quantization matrices. The invention includes several techniques and tools, which can be used in combination or separately. For example, the audio encoder can generate quantization matrices from critical band patterns for blocks of audio data. The encoder can compute the quantization matrices directly from the critical band patterns, which can be computed from the same audio data that is being compressed. The audio encoder/decoder can use different modes for generating/applying quantization matrices depending on the coding channel mode of multi-channel audio data. The audio encoder/decoder can use different compression/decompression modes for the quantization matrices, including a parametric compression/decompression mode.

Abstract: An audio encoder regulates quality and bitrate with a control strategy. The strategy includes several features. First, an encoder regulates quantization using quality, minimum bit count, and maximum bit count parameters. Second, an encoder regulates quantization using a noise measure that indicates reliability of a complexity measure. Third, an encoder normalizes a control parameter value according to block size for a variable-size block. Fourth, an encoder uses a bit-count control loop de-linked from a quality control loop. Fifth, an encoder addresses non-monotonicity of quality measurement as a function of quantization level when selecting a quantization level. Sixth, an encoder uses particular interpolation rules to find a quantization level In a quality or bit-count control loop. Seventh, an encoder filters a control parameter value to smooth quality. Eighth, an encoder corrects model bias by adjusting a control parameter value in view of current buffer fullness.