Method for choosing rate control parameters in motion-compensated transform-based picture coding scheme using non-parametric technique

• US 6,292,589 B1
• Filed: 06/21/1996
• Issued: 09/18/2001
• Est. Priority Date: 06/21/1996
• Status: Expired due to Term

• Current Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Original Assignee: Compaq Computer Corporation (HP Inc.)
• Inventors: Rose, Robert C., Chow, King-Wai
• Primary Examiner(s): CHANG, JON CARLTON
• Application Number:
  US08/668,153
• Time in Patent Office: 1,915 Days
• Field of Search: 382/238, 382/239, 382/250, 382/251, 348/405, 348/413, 348/415, 348/416, 348/412.1, 348/413.1, 348/415.1, 348/416.1
• US Class Current: 382/239
• CPC Class Codes: H04N 19/107 between spatial and temporal predictive coding, e.g. picture refresh
• CPC Class Codes: H04N 19/126 Details of normalisation or weighting functions, e.g. normalisation matrices or variable uniform quantisers
• CPC Class Codes: H04N 19/127 Prioritisation of hardware or computational resources
• CPC Class Codes: H04N 19/132 Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking
• CPC Class Codes: H04N 19/137 Motion inside a coding unit, e.g. average field, frame or block difference
• CPC Class Codes: H04N 19/15 by monitoring actual compressed data size at the memory before deciding storage at the transmission buffer
• CPC Class Codes: H04N 19/157 Assigned coding mode, i.e. the coding mode being predefined or preselected to be further used for selection of another element or parameter
• CPC Class Codes: H04N 19/159 Prediction type, e.g. intra-frame, inter-frame or bidirectional frame prediction
• CPC Class Codes: H04N 19/172 the region being a picture, frame or field
• CPC Class Codes: H04N 19/176 the region being a block, e.g. a macroblock
• CPC Class Codes: H04N 19/196 being specially adapted for the computation of encoding parameters, e.g. by averaging previously computed encoding parameters processing of motion vectors H04N19/513
• CPC Class Codes: H04N 19/197 including determination of the initial value of an encoding parameter H04N19/56 takes precedence
• CPC Class Codes: H04N 19/198 including smoothing of a sequence of encoding parameters, e.g. by averaging, by choice of the maximum, minimum or median value
• CPC Class Codes: H04N 19/42 characterised by implementation details or hardware specially adapted for video compression or decompression, e.g. dedicated software implementation H04N19/635 takes precedence
• CPC Class Codes: H04N 19/423 characterised by memory arrangements H04N19/433 takes precedence
• CPC Class Codes: H04N 19/587 involving temporal sub-sampling or interpolation, e.g. decimation or subsequent interpolation of pictures in a video sequence
• CPC Class Codes: H04N 19/61 in combination with predictive coding
• CPC Class Codes: H04N 19/82 involving filtering within a prediction loop

Abstract

A method of assigning quantization values for use during the compression of images is disclosed. The method includes constructing a non-parametric model during a training phase based on relationships between image characteristics, quantization values, and required bit resources to encode images. The model is built by considering a wide sample of images. The consideration includes determining the temporal and spatial characteristics of the images, compressing the images over a range of quantization values, and recording the resultant required bit resource on a per characterization/quantization level basis. Once built, the model may be used during real time compression by determining the characteristics of the input image and using the allocated resource to find a corresponding match in the non-parametric model. The associated quantization value is then assigned to the input image.

Claims

1. A method for choosing quantization values used during compression of image data comprising the steps of:

a) constructing a non-parametric model of relationships between required bit resource allocation for compression, image characterization, and quantization values for a plurality of sample images, said non-parametric model being constructed during a training phase which is not said compression of image data and including determining temporal and spatial characteristics of the sample images;

b) for image data compression, receiving as an input, a macroblock associated with a particular image, the particular image including several macroblocks;

c) characterizing contents of said macroblock as being one of a plurality of image types of image characterization, said image characterization being representable as a scalar value;

d) making said bit resource allocation by allocating a subset of bit resources associated with said particular image to said macroblock;

e) receiving by said non-parametric model, said image characterization and said bit resource allocation;

f) matching said image characterization and said bit resource allocation of said macroblock to associated values in said non-parametric model; and

g) assigning a particular one of said plurality of said quantization values, associated with said matched image characterization and bit resource allocation, to said macroblock.

2. A method for choosing quantization values used during compression of image data comprising the steps of:

a) constructing a non-parametric model of relationships between required bit resource allocation for compression, image characterization, and quantization values for a plurality of sample images, said non-parametric model being constructed during a training phase which is not said compression of image data;

b) for image data compression, receiving as an input, a macroblock associated with a particular image, the particular image including several macroblocks;

c) characterizing contents of said macroblock as being one of a plurality of image types of image characterization, said image characterization being representable as a scalar value;

d) making said bit resource allocation by allocating a subset of bit resources associated with said particular image to said macroblock;

e) receiving by said non-parametric model, said image characterization and said bit resource allocation;

f) matching said image characterization and said bit resource allocation of said macroblock to associated values in said non-parametric model;

g) assigning a particular one of said plurality of said quantization values, associated with said matched image characterization and bit resource allocation, to said macroblock;

h) compressing said macroblock, using said particular one of said quantization values, such that a resultant compressed macroblock is represented by a subset of bits used to represent said macroblock;

i) performing steps b) through g) and said compression step for a plurality of macroblocks associated with said particular image;

j) totaling said subset of bits associated with each of said plurality of macroblocks;

k) comparing said total of subset of bits with said bit resource allocation associated with said plurality of macroblocks; and

l) scaling, based on the comparison of said total of subset of bits with said bit resource allocation associated with said plurality of macroblocks, a subsequent bit resource allocation for a corresponding subsequent plurality of macroblocks.